The Impact of COVID-19 on Student Experiences and Expectations: Evidence from a Survey

In order to understand the impact of the COVID-19 pandemic on higher education, we surveyed approximately 1,500 students at one of the largest public institutions in the United States using an instrument designed to recover the causal impact of the pandemic on students' current and expected outcomes. Results show large negative effects across many dimensions. Due to COVID-19: 13% of students have delayed graduation, 40% lost a job, internship, or a job offer, and 29% expect to earn less at age 35. Moreover, these effects have been highly heterogeneous. One quarter of students increased their study time by more than 4 hours per week due to COVID-19, while another quarter decreased their study time by more than 5 hours per week. This heterogeneity often followed existing socioeconomic divides; lower-income students are 55% more likely to have delayed graduation due to COVID-19 than their higher-income peers. Finally, we show that the economic and health related shocks induced by COVID-19 vary systematically by socioeconomic factors and constitute key mediators in explaining the large (and heterogeneous) effects of the pandemic.

Noah Deitrick and Adam Streff provided excellent research assistance. All errors that remain are ours. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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Original research article, impacts of the covid-19 pandemic on student learning and opportunity gaps across the 2020–2021 school year: a national survey of teachers.

research paper about the impact of covid 19 on education

  • 1 School of Education, University of Delaware, Newark, DE, United States
  • 2 Department of Teaching, Learning, and Culture, Texas A&M University, College Station, TX, United States
  • 3 School of Education, University of California, Irvine, Irvine, CA, United States
  • 4 Department of Special Education and Communication Disorders, University of Nebraska-Lincoln, Lincoln, NE, United States

Although many school districts made efforts to provide instruction during the COVID-19 pandemic (including in-person, remote, and blended/hybrid options the length of instruction time and delivery models have varied from district to district. This disruption in education has been projected to result in a significant learning loss, which may be particularly profound for students from disadvantaged backgrounds, leading to widening opportunity gaps. However, there is limited empirical data that can provide important contextual background for understanding the impact of the pandemic on student learning. Therefore, we conducted a national survey with a random sample of 582 elementary school teachers to understand the instructional changes that occurred, the amount of academic content instruction provided to students, and teachers perceptions of the learning supports needed and provided to students during the 2020–2021 school year. Results indicated that most teachers relied on alternative forms of instruction and experienced changes in delivery models but reported low instructional effectiveness. Compared to typical years, teachers reported significant decreases in curriculum coverage; the number of students who received needed interventions, and students who were ready to transition to the next grade level during the 2020–2021 school year. Teachers also reported greater impacts on instruction for students from disadvantaged backgrounds. Follow-up analyses using prior school achievement data corroborated the findings that higher school achievement was associated with smaller impacts on student learning and delivery of instruction.

Introduction

Many school districts were forced to temporarily close schools in spring 2020 during the COVID-19 crisis. This marked one of the largest disruptions to education in history, forcing more than 1.6 billion children out of school in the United States and affecting 95% of school-aged children worldwide ( United Nations, 2020 ; Kaffenberger, 2021 ). Although many schools attempted to provide remote instruction during the spring of 2020, estimates suggest that between 7.2 and 11.6 million K-5 students also may not have received remote instruction ( Goodrich et al., 2022 ).

In the 2020–2021 school year, school districts adapted to the pandemic by developing a variety of instructional models to reach students, including remote learning, blended/hybrid learning, and in-person learning, with some school districts implementing multiple models for various lengths of time. Despite the strong efforts of schools and teachers, these delivery models may not have provided the same quality of education compared to instruction prior to the pandemic for several reasons. Schools that returned to in-person learning faced frequent student absences and staff shortages due to the COVID related quarantines. For schools that opted to provide alternate forms of learning, many teachers, parents, and students had to make quick transitions without strong supports in place (e.g., technology support, student engagement strategies; Stanistreet et al., 2020 ).

To estimate the potential impact of the COVID-19 school closures on student learning (e.g., Dorn et al., 2020 ; Kuhfeld et al., 2020 ; Kaffenberger, 2021 ), some researchers have projected learning losses based on estimates from typical school closures, such as summer breaks. With some variability in the estimates of the summer learning loss (0.001 to 0.01 SD learning loss per day out of school), prior research indicated student achievement slows down or even declines over the summer breaks (e.g., von Hippel et al., 2018 ; Kuhfeld, 2019 ). Based on these estimates, Kuhfeld et al. (2020) projected that students who did not have access to remote instruction (3 months) in spring 2020 would begin fall 2020 with only 37% to 68% of typical learning gains in reading and mathematics, and some students may be up to one year behind in mathematics. Even students who received remote instruction in spring 2020 were projected to begin fall 2020 with 60% to 87% of their typical learning gains.

However, these numbers may underestimate the problem to some degree. The assumption is that learning losses could be similar to losses experienced during other breaks from school. However, instructional challenges related to COVID-19 are also likely to have resulted in less content coverage when school has been in session, compounding the losses. In addition, differential access to technology and remote instruction during COVID-19 school closures are projected to exacerbate the impacts of the pandemic for some populations, widening SES-based opportunity gaps. The learning losses are expected to be greatest among low-income students because students from high SES schools were estimated to receive more remote instruction than students from low SES schools ( Kuhfeld et al., 2020 ). Even when students from low SES schools were able to access remote instruction, they were less likely to have the same high-quality remote learning or supportive environments (e.g., parental academic supervision, space with minimal distraction; Dorn et al., 2020 ). Dorn et al. (2020) projected that low-income students would experience 12.4 months of learning loss compared to the overall average learning loss of 6.8 months, exacerbating the existing opportunity gaps by 15% to 20%.

Some researchers have suggested that the short-term learning losses due to the pandemic may be cumulative and result in larger and permanent learning losses (e.g., Dorn et al., 2020 ; Kaffenberger, 2021 ). Dorn et al. (2020) estimated that the pandemic is likely to lead to higher high-school dropout rates (i.e., 2–9% increase to the current 5% rate) due to decreased academic engagement and achievement, and disruptions to supports that can help students stay in school (e.g., community support, youth-serving organizations), leading to long-term economic issues. Kaffenberger (2021) reported that learning loss in grade 3 would accumulate and result in students performing 1 to 1.5 years lower in grade 10. He also estimated that short-term remediation efforts (e.g., teachers covering 1/2 of grade 3 curriculum in grade 4 and reverting to the pre-pandemic curriculum and instructional levels by grade 5) would reduce the long-term learning loss to one-half of a school year. The long-term remediation efforts (e.g., identifying students’ learning levels via formative assessments, adapting teacher instructions) were estimated to fully mitigate the learning loss. That said, the pandemic is still on-going (with some school closures occurring again in early 2022 due to COVID-19 variants), and it is unlikely that schools and teachers were able to cover the same amount of content in the 2020–2021 school year as in typical years, or that they were able to provide the same levels of support to students from minoritized and disadvantaged populations that they do in typical years.

Despite these projections, the empirical data to evaluate the actual impact of the COVID-19 pandemic on student learning is limited. Engzell et al. (2021) used national assessments conducted before (January to February) and after (June) the COVID-19 lockdown in the Netherlands. They compared student progress in mathematics, reading, and spelling on the national assessments during 2020 to student progress in the three previous years. Results indicated a learning loss equivalent to 3 percentile points despite the relatively short lockdown. However, the learning loss was up to 60% greater among students from disadvantaged backgrounds (i.e., students from less-educated households), indicating the disproportionate impact of the pandemic on student learning. Similarly, achievement scores on state assessments for students in grades 4 to 8 in 17 school districts in Illinois indicated that students scored significantly lower than expected in mathematics compared to prior to the pandemic, resulting in a learning losses as large as 56% of a school year ( Streich et al., 2021 ). Furthermore, special education status, English language learner status, and eligibility for free/reduced price lunch were associated with greater learning losses in mathematics among middle school students.

Taken together, although prior research has shown varying levels of impact of the COVID-19 pandemic on student learning, it is evident that student learning was disrupted, leading to short-term and long-term detrimental effects on student achievement and educational attainment. Prior research also suggests that this learning loss may be particularly profound for students from disadvantaged backgrounds, leading to widening opportunity gaps. However, the current literature and our understanding of the impact of the pandemic on student learning is primarily based on model-based projections and limited empirical data comparing student performance prior to and after the lockdown. Detailed empirical data that can provide important contextual background for understanding the impact of the pandemic on student learning are missing.

Therefore, the purpose of the present study was to understand the instructional changes that occurred during the 2020–2021 school year and their impact on student learning from a national sample of elementary school teachers. Additionally, we sought information from teachers regarding the amount of academic content instruction provided to students and teachers’ perceptions of the learning supports needed and provided to students across the 2020–2021 school year. Furthermore, we aimed to explore whether teacher reported changes were related to prior school achievement data.

Materials and Methods

Participants.

For survey distribution, we obtained a representative random sample of K-5 educator email addresses that was proportionally reflective of the number of teachers in each grade (K-5) as well as representative of the distribution of the United States population across different geographic regions survey distribution from Market Data Retrieval (MDR). We made sure that only one teacher from each school was selected to maximize the number of schools. We calculated the total number of respondents ( N = 382) needed to achieve a margin of error of ± 5.0% with a 95% confidence interval ( Dillman, 2000 ). After excluding 289 invalid email address, we sent 9,476 teachers the invitation to complete the survey. Of those, 595 teachers provided consent, and 13 teachers who did not answer any questions were later excluded. The final sample consisted of 582 teachers, providing this survey with a ± 4.1% margin of error with a 95% confidence interval.

Stanford Education Data Archive (SEDA; Reardon et al., 2021 ) provides demographics (e.g., region, gender, socioeconomic status, race) and academic achievement data (e.g., mathematics, English language arts) for all tested students in grades 3–8 in public schools across the United States averaged over the 2008–2009 to 2017–2018 school years. SEDA school-level mean mathematics and English language arts achievement data were available for 490 teachers, and covariate data were available for 515 teachers who responded to our survey. Given the lower than anticipated response rate (6.14%), we compared teachers who did and did not respond to the survey to ensure the generalizability of our findings. After correcting for Type I error rate, there were some statistically significant differences by geographic region. The Mid-Atlantic and South-Central regions were significantly under-represented in survey responders, whereas the Mountain and North-Central regions were significantly over-represented among survey responders. There were no other significant differences. We also compared our sample of teachers to national teacher demographics reported by the National Center for Educational Statistics ( Hussar et al., 2020 ). Overall, our sample approximated the national averages in terms of gender and race. However, teachers with over 20 years of teaching experience were over-represented in our samples (32.3%) compared to the national average (22.4%).

Survey Questions

We created and administered the survey using the Qualtrics electronic survey platform. The survey consisted of 59 items. The first eight questions were on demographics of teachers and students in their classrooms. Next, teachers answered questions about the instructional model(s) used by their schools. Additionally, we asked questions related to student progress and instruction in three specific academic content areas: reading, mathematics, and writing. The questions included the amount of planned curriculum teachers were able to cover, percentage of students needing extra support in each academic area, percentage of students who did not receive needed support for each academic area during the 2020–2021 school year compared to typical years prior to the COVID pandemic, and whether these changes were due to the pandemic. Teachers also rated the negative impacts the pandemic had on students overall, as well as on subpopulations of students (i.e., students from low-income backgrounds, students with IEPs, students who are English language learners). They also rated their perceived effectiveness of remote instruction. Finally, teachers answered questions about their opinions regarding the effectiveness of instruction during the pandemic.

Overall, our respondents had a mean of 15.44 years of teaching experience ( SD = 9.65) and a mean of 23 students in their class ( SD = 9.51) at the time of the survey. The majority (80.2%) reported having less than 20% of students with IEPs in their classroom. Similarly, 79.5% of teachers reported having classrooms with less than 20% English language learners.

Descriptive Analysis

Instructional model.

Figure 1 shows the instructional models teachers reported for their schools at the start and end of the 2020–2021 school year. At the start of the school year, most schools offered either 100% remote instruction (46.7%) or in-person instruction with an option for remote instruction (30.9%). Approximately 12.1% of schools offered hybrid, and only 8.6% of schools offered 100% in-person instruction. However, approximately 60% of teachers experienced a change in their instructional model from the beginning to the end of the school year. At the end of the school year, most schools offered in-person instruction with an option for remote instruction (65.2%), followed by 100% in-person (16.5%), hybrid (13.0%), and 100% remote (2.6%) instruction. Thus, the number of schools offering 100% in-person or in-person instruction with an option for remote instruction doubled from the beginning to the end of the 2020–2021 school year.

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Figure 1. Teacher-reported school instructional models.

More specifically, 64.7% of teachers indicated that their instruction was 100% in person at least part of the 2020–2021 school year whereas 35.3% of teachers indicated that they never offered 100% in-person instruction. Among teachers who reported using a 100% in-person instructional model for at least part of the year, the percentage of the school year for which their school provided 100% in-person instruction varied: less than 20% of the school year (16.3%), between 21 and 40% (19.3%), between 41 and 60% of the year (15.2%), between 80 and 99% (15.8%), and 100% (19.0%).

Student Progress and Instruction in Academic Content Areas

Curriculum coverage.

Overall, teachers reported a significant decrease in the amount of planned curriculum they were able to cover in academic content areas (i.e., reading, mathematics, and writing) during the 2020–2021 school year compared to typical years. Figure 2 shows the percentage of planned curriculum teachers were able to cover in each academic area. During typical years prior to the COVID-19 pandemic, 93.3% of teachers indicated that they were able to cover more than 80% of planned curriculum in reading compared to only 43.8% of teachers during the 2020–2021 school year. In other words, more than half the teachers who responded to the survey (56.3%) were not able to cover 80% of their planned reading curriculum during the 2020–2021 school year, compared to only 6.7% of teachers during typical years. This pattern of findings was similar for mathematics. Only 53.2% of teachers reported that they were able to cover more than 80% of their planned curriculum in mathematics compared to 92.8% of teachers in typical years. For writing, about 30.9% of teachers indicated that they were able to cover more than 80% of planned curriculum during the 2020–2021 school year compared to 79.5% of teachers during typical years. Most teachers (85.4%) indicated that this change in their ability to cover the curriculum during the 2020–2021 school year was due to the pandemic. Other reasons reported by 4.8% of teachers included student absences, having a new administration team, and other natural disasters in addition to the pandemic.

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Figure 2. Percentage of curriculum covered in each academic area.

Students Needing Extra Support/Intervention

Teachers indicated that fewer students who needed extra support and/or intervention in academic content areas actually received the support during the 2020–2021 school year compared to typical years. During typical years, teachers reported students were able to receive extra support/intervention they needed in reading (74.9%), mathematics (71.2%), and writing (70.2%). However, there was a significant decrease in the percentage of teachers who indicated that students received the needed support during the 2020–2021 school year: 44.3% in reading, 49.2% in mathematics, and 41.9% in writing.

Student Readiness for Transition

Teachers reported fewer students were ready to transition to the next grade level at the end of 2020–2021 school year compared to typical years (see Figure 3 ). Whereas 68.9% of teachers indicated at least 80% of their students being ready to transition to the next grade in typical years, only about 31.9% of teachers reported at least 80% of their students were ready to transition to the next grade at the end of the 2020–2021 school year. About 29.4% of teachers indicated that less than 60% of their students were ready to transition to the next grade level compared to only 4.5% of teachers indicating less than 60% of their students ready to transition in typical years. The majority of teachers (65.5%) indicated that this drop in the percentage of students ready to transition to the next grade was due to the COVID-19 pandemic. A small portion of respondents (6.2%) indicated other reasons, which included a lack of student participation, lack of teacher knowledge, and lack of high-quality instruction.

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Figure 3. Percentage of students ready to transition to next grade.

Subpopulations of Students

Teachers rated the impact of the pandemic on their delivery of academic skills instruction on a 0 (no impact, delivery of academic instruction was typical) to 10 (high impact, students missed significant instructional time, delivery of instruction was very challenging, many students are behind) scale. Overall, the mean rating was 6.67 ( SD = 2.64), indicating a moderate to large impact of the pandemic on teachers’ delivery of academic instruction. Teachers indicated significantly greater impacts for students from low-income backgrounds ( M = 7.74, SD = 2.59) compared to those who were not from low-income backgrounds ( M = 4.83, SD = 2.59), t (457) = 24.04, p < 0.001. Teachers also rated significantly greater impacts for students with IEPs ( M = 7.43, SD = 2.90) compared to those without IEPs ( M = 5.51, SD = 2.71), t (455) = 15.64, p < 0.001. Finally, teachers rated significantly greater impacts for English language learners ( M = 7.31, SD = 2.88) compared to non-English language learners ( M = 5.45, SD = 2.84), t (389) = 13.78, p < 0.001.

Overall, teachers rated that remote instruction was significantly less effective for students from disadvantaged backgrounds. Teachers rated remote instruction being more effective for students who were not from low-income backgrounds ( M = 5.66, SD = 2.51) compared to students from low-income backgrounds ( M = 4.13, SD = 3.02), t (432) = −10.17, p < 0.001. Teachers also rated that remote instruction was more effective for students without IEPs ( M = 5.45, SD = 2.43) than it was for those with IEPs ( M = 3.84, SD = 3.05), t (421) = −11.22, p < 0.001. Lastly, teachers rated that remote instruction was significantly more effective for students who were not English language learners ( M = 5.45, SD = 2.53) compared to English language learners ( M = 3.92, SD = 3.05), t (356) = −9.13, p < 0.001.

Inferential Analysis

Zero-order correlations.

Our third research question focused on the relations between school achievement indexed by SEDA and various survey questions, including use of a 100% in-person instructional model, percentage of students ready to transition to the next grade level in Spring 2021, overall impact of the pandemic on academic skills instruction, and the impact of the pandemic on teachers’ ability to cover the curriculum and provide intervention for specific academic skills. There was a small correlation between school achievement and the percentage of time in which a 100% in-person instructional model was used ( r = 0.19, p < 0.001), indicating higher achieving schools provided 100% in-person instruction more often than low achieving schools.

School achievement was moderately negatively correlated with overall ratings of the impact of the pandemic ( r = −0.29, p < 0.001) and with teacher-reported impacts of the pandemic on the percentage of students ready to transition to the next grade level ( r = −0.30, p < 0.001). This pattern of results indicated that teachers at higher achieving schools reported fewer negative effects of the pandemic, and teachers at higher achieving schools reported smaller differences in the number of students ready to transition to the next grade level between the 2020 and 2021 school year and typical years prior to the pandemic. School achievement was also correlated with teacher-reported impacts of the pandemic on specific academic content areas, but these correlations were small ( r s range from −0.11 to −0.19, all p s < 0.05). There were small correlations between the percent of the year a 100% in-person instructional model was used and teacher-reported impacts of the pandemic ( r s range from −0.18 to −0.22, all p s < 0.001), indicating that teachers who used more in-person instruction reported smaller impacts of the pandemic on their ability to cover the curriculum and the percentage of students who needed supplemental intervention for academic skills instruction.

Regression Analysis

To further evaluate our third research question, we examined predictors of the overall impact of the pandemic and the amount of the curriculum that was covered in reading, writing, and mathematics in the 2020–2021 school year, including SEDA school mean achievement and percentage of time in which a 100% in-person instructional model was used. Regression models predicting amount of curriculum covered in the 2020–2021 school year controlled for teacher reports of the amount of curriculum covered in typical years. Results are presented in Table 1 . We note that negative correlations for overall impact indicate that more in-person instruction and higher achieving schools experienced fewer negative effects of the pandemic. Positive correlations for coverage of reading, writing, and mathematics curriculum indicate that more in-person instruction and higher achieving schools were associated with covering more of the planned curriculum for academic skills. Both school achievement and percentage of time using a 100% in-person instructional model were significantly predictive of overall impacts of the pandemic and teacher reported coverage of the reading, writing, and mathematics curriculum, even after controlling for teacher reported coverage of the curriculum in typical years. Higher school achievement and more use of a 100% in-person instructional model were associated with smaller negative impacts of the pandemic and greater coverage of academic curricula.

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Table 1. Standardized regression coefficients predicting overall impact and coverage of curriculum.

Finally, we used logistic regression analysis to examine whether SEDA school achievement and percentage of time using a 100% in-person instructional model predicted whether there were students who needed extra intervention in reading, writing, and mathematics but did not receive it in the 2020–2021 school year, after controlling for whether there were students who needed extra intervention but did not receive it in typical years. These results are presented in Table 2 . Use of a 100% in-person instructional model was only significant for mathematics, indicating that teachers who reported using more 100% in-person instruction were less likely to report having students who needed extra mathematics intervention but did not receive it; however, the magnitude of this effect was small. In contrast, higher achieving schools were significantly less likely than lower achieving schools to have students who needed additional intervention but did not receive it, even after controlling for students needing but not receiving intervention in typical years.

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Table 2. Logistic regression models predicting whether students who needed additional supports for academic skills did not receive them.

Successes and Challenges of Instruction

For questions related to the successes and challenges of remote and in-person instruction during the 2020–2021 school year, teachers were allowed to indicate multiple items (i.e., check all that apply). Teachers indicated that having a lower teacher-student ratio would contribute to successful remote instruction (61.7%) followed by the structures and scheduling of remote instruction (52.6%), training opportunities (45%), and support personnel (e.g., paraprofessionals, 38.5%). The majority of other responses included having parental support at home and students’ access to better technology (internet access, remote instruction platform support), and having a teacher dedicated to remote instruction.

Teachers also indicated that distractions in students’ homes (71.1%), internet access/availability (61.0%), student attendance (60.8%), lack of face-to-face interactions with students (57.7%), difficulty with evaluating student work (55.7%), difficulty with monitoring student progress (48.8%), managing remote and in-person instruction simultaneously (42.6%), and difficulty with providing feedback on student work (40.7%) as challenges associated with delivering remote instruction. Other challenges included a lack of parental support/involvement, lack of student engagement, and parents or other household members completing student assignments or assessments.

Despite these challenges, teachers indicated that some positive takeaways during the 2020–2021 school year were students being more conscientious (68.6%), greater ability to provide individualized attention due to reduced class sizes or alternating days (21.6%), and more time for students to participate in academics due to reductions in extracurricular activities (21.1%). About 24.6% of teachers indicated that there were no positive takeaways from the 2020–2021 school year.

The results of this survey provide important context about the instructional models used by schools during the 2020–2021 school year, how content coverage may compound issues related to learning losses in academic areas, and factors that may be related to the ability of schools to cover content and support students. Several studies have demonstrated that student achievement has been lower during the pandemic compared to prior to the pandemic, with estimates ranging from three percentile points in the Netherlands ( Engzell et al., 2021 ) to more than half of a school year in the U.S. state of Illinois ( Streich et al., 2021 ). Moreover, students’ academic motivation and participation in extracurricular activities, as perceived by their parents, decreased significantly during the COVID-19 pandemic ( Zaccoletti et al., 2020 ).

Yet, the pandemic is not over, and with the continued struggle with the COVID variants in 2022 currently, students may be falling even further behind. The results of this survey suggest that most teachers were not able to cover at least 80% of their reading, writing, and mathematics curriculum, which was significantly lower than their reported ability to cover 80% of the curriculum in previous years. Teachers also clearly indicated that many fewer students were ready to transition to the next grade level at the end of the 2020–2021 school year. Using average reported class sizes and teacher responses for students not ready to transition to the next grade level, we estimated that 32.4% of students were not ready to transition, as compared to 13.9% in previous years (an increase of 18.5%). With 21.2 million K-5 students attending school in 2020 ( National Center for Education Statistics, 2021 ), this means nearly 3.9 million more students (6.8 million total) were not ready to transition to the next grade, with likely disproportionate impacts on minoritized students.

Our findings also indicated that many students who needed extra support/intervention in the academic content areas did not receive needed support in the 2020–2021 school year. This is alarming because it has likely compounded learning losses already realized during school shutdowns in spring of 2020, and some schools still may not be fully covering the academic curriculum in the 2021–2022 school year. This suggests there will be long term and compounded effects if teachers continue to have difficulty implementing the full curriculum. Therefore, our findings call for immediate recovery efforts.

Kaffenberger (2021) projected that short-term (e.g., covering previous year’s curriculum before revering to the pre-pandemic curriculum) and long-term efforts (e.g., identifying students’ needs using formative assessments, adapting teacher instruction to students’ levels and needs) can reduce/remediate the learning loss. Therefore, substantial restructuring of current pre-pandemic curricula may be inevitable to minimize the compounded effects. In addition, some states have initiated alternative ways to offer additional instruction (e.g., Tennessee Tutoring Coprs). Continued efforts should be made to find alternative and innovative ways to provide additional learning opportunities to remediate the learning loss. Beyond the immediate educational needs, Fusco et al. (2021) suggested providing career support for students to better prepare them for the economic crisis and changes following the COVID-19 pandemic.

Our survey results indicated that most teachers relied on alternative forms of instruction and experienced changes during the 2020–2021 school year. Yet, the overall rating for teacher-reported effectiveness of remote instruction was low ( M = 4.74). This finding suggests that continued development of high-quality online educational learning and support is also needed. Moreover, Zhu and Liu (2020) called for more quantitative and qualitative research to evaluate remote teaching and learning, and long-term sustainability. Consistent with teachers’ reports in our survey, as well as in Goodrich et al. (2022) , more systematic training for school personnel is needed to improve the quality of remote instruction. Additionally, prior studies have found that family, school, and peer support increases student engagement, which in turn improves academic competence and achievement (e.g., Elias and Haynes, 2008 ; Estell and Perdue, 2013 ). As much as in-person school engagement is important to academic achievement and school completion, student engagement during remote instruction may be critical to promoting successful remote learning. Teachers who responded to our survey did note a lack of student engagement and parental support/involvement as a challenge to providing remote instruction. Such support from family, school, and peers may be especially important for students from disadvantaged backgrounds ( Elias and Haynes, 2008 ).

Our survey results also add to the growing literature that the impacts of the COVID-19 pandemic on academic learning have disproportionately affected low-income students, minoritized students, and students with disabilities (see Dorn et al., 2020 ; Goodrich et al., 2022 ). In the current survey, teachers reported greater impacts of the pandemic on academic instruction for students with IEPs, low-income students, and English language learners. Our regression analyses corroborate these findings across schools as well, as higher school achievement was associated with smaller negative impacts on the curriculum coverage and fewer students requiring additional intervention. Our results also indicate that teachers in higher performing schools did not have to alter their instruction as much as teachers in lower performing schools. This may have played a role in the reported curriculum coverage and associated learning losses, as our results indicated that the amount of in-person instruction significantly contributed to teachers’ ability to cover the curriculum. These findings are important to consider when allocating resources for pandemic recovery efforts. Moreover, the COVID-19 induced economic damage and educational budget cuts are likely to have a greater impact on students from disadvantaged backgrounds. Recovery efforts should be considered carefully, so that they do not reinforce existing inequalities.

Our findings also add to the literature in an important way by providing teachers with an opportunity to identify other factors that may have contributed to their ability to cover the curricula and support their students. This can offer Federal and State Departments of Education with areas of opportunity for providing teachers with support, funding, or intervention resources. For example, teachers consistently reported that personnel and training resources can contribute to better implementation of instruction (including remote instruction). Solutions might include increasing the number of paraprofessionals to assist with instruction and/or providing training opportunities to teachers and paraprofessionals.

Positive Take-Aways and Potential Solutions

Approximately 75% of teachers indicated that there were also some positives that came out of the pandemic, including increases in student conscientiousness, prioritization of some academic content, and systems that resulted in more individual attention. Policymakers and administrators may want to consider thinking more flexibly about school schedules and supports for teachers and students moving forward. Alternating days for instruction for students to reduce class sizes may not be desirable or feasible in the long-term, but there may be other creative approaches to continue capitalize on the benefits of smaller student groupings, such as staggering start and end times for the school day.

Limitations

The samples of teachers who completed our surveys were generally representative of the population of teachers in the United States However, a large percentage of teachers did not respond to the surveys. Although responders and non-responders were similar in key demographic variables (e.g., SES, school setting, school type, grade level taught), it is possible that low response rate resulted in selection bias. It is also possible that the teachers may have under- or over-estimated other descriptive variables for their classrooms or were unaware of some of the school services provided by resource and special education teachers.

Implications and Conclusion

Schools in the United States have a large problem on their hands. Along with learning losses, many teachers report not covering as much of the academic curricula for students, especially in schools with lower achievement levels. This is an ongoing problem that is likely to be exacerbated, and it will likely continue to widen the opportunity gaps for minoritized students, low-income students, and students with disabilities. Policymakers, school administrators, and teachers must be cognizant of the challenges with implementing instruction consistently to adequately cover the necessary content each year, and even increase the content coverage and student support to accelerate recovery efforts. Of course, these considerations need to be weighed against public health safety, which is an important factor in deciding which educational models to implement. It will also be important for educational decision makers to consider these teacher report findings when allocating recovery resources, such as prioritizing lower achieving schools and students from disadvantaged backgrounds.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

The studies involving human participants were reviewed and approved by IRB at the University of Nebraska-Lincoln. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

JN, JG, MH, and NK were equally responsible for the conduct of this research. All authors helped formulate the research questions to be included in surveys, assisted with survey distribution, data cleaning and analysis, and writing survey results for publication.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords : COVID-19, survey research, elementary school, academic instruction, opportunity gaps

Citation: Namkung JM, Goodrich JM, Hebert M and Koziol N (2022) Impacts of the COVID-19 Pandemic on Student Learning and Opportunity Gaps Across the 2020–2021 School Year: A National Survey of Teachers. Front. Educ. 7:921497. doi: 10.3389/feduc.2022.921497

Received: 15 April 2022; Accepted: 10 June 2022; Published: 07 July 2022.

Reviewed by:

Copyright © 2022 Namkung, Goodrich, Hebert and Koziol. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Jessica M. Namkung, [email protected]

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Research Article

COVID-19’s impacts on the scope, effectiveness, and interaction characteristics of online learning: A social network analysis

Roles Data curation, Formal analysis, Methodology, Writing – review & editing

¶ ‡ JZ and YD are contributed equally to this work as first authors.

Affiliation School of Educational Information Technology, South China Normal University, Guangzhou, Guangdong, China

Roles Data curation, Formal analysis, Methodology, Writing – original draft

Affiliations School of Educational Information Technology, South China Normal University, Guangzhou, Guangdong, China, Hangzhou Zhongce Vocational School Qiantang, Hangzhou, Zhejiang, China

Roles Data curation, Writing – original draft

Roles Data curation

Roles Writing – original draft

Affiliation Faculty of Education, Shenzhen University, Shenzhen, Guangdong, China

Roles Conceptualization, Supervision, Writing – review & editing

* E-mail: [email protected] (JH); [email protected] (YZ)

ORCID logo

  • Junyi Zhang, 
  • Yigang Ding, 
  • Xinru Yang, 
  • Jinping Zhong, 
  • XinXin Qiu, 
  • Zhishan Zou, 
  • Yujie Xu, 
  • Xiunan Jin, 
  • Xiaomin Wu, 

PLOS

  • Published: August 23, 2022
  • https://doi.org/10.1371/journal.pone.0273016
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Table 1

The COVID-19 outbreak brought online learning to the forefront of education. Scholars have conducted many studies on online learning during the pandemic, but only a few have performed quantitative comparative analyses of students’ online learning behavior before and after the outbreak. We collected review data from China’s massive open online course platform called icourse.163 and performed social network analysis on 15 courses to explore courses’ interaction characteristics before, during, and after the COVID-19 pan-demic. Specifically, we focused on the following aspects: (1) variations in the scale of online learning amid COVID-19; (2a) the characteristics of online learning interaction during the pandemic; (2b) the characteristics of online learning interaction after the pandemic; and (3) differences in the interaction characteristics of social science courses and natural science courses. Results revealed that only a small number of courses witnessed an uptick in online interaction, suggesting that the pandemic’s role in promoting the scale of courses was not significant. During the pandemic, online learning interaction became more frequent among course network members whose interaction scale increased. After the pandemic, although the scale of interaction declined, online learning interaction became more effective. The scale and level of interaction in Electrodynamics (a natural science course) and Economics (a social science course) both rose during the pan-demic. However, long after the pandemic, the Economics course sustained online interaction whereas interaction in the Electrodynamics course steadily declined. This discrepancy could be due to the unique characteristics of natural science courses and social science courses.

Citation: Zhang J, Ding Y, Yang X, Zhong J, Qiu X, Zou Z, et al. (2022) COVID-19’s impacts on the scope, effectiveness, and interaction characteristics of online learning: A social network analysis. PLoS ONE 17(8): e0273016. https://doi.org/10.1371/journal.pone.0273016

Editor: Heng Luo, Central China Normal University, CHINA

Received: April 20, 2022; Accepted: July 29, 2022; Published: August 23, 2022

Copyright: © 2022 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data underlying the results presented in the study were downloaded from https://www.icourse163.org/ and are now shared fully on Github ( https://github.com/zjyzhangjunyi/dataset-from-icourse163-for-SNA ). These data have no private information and can be used for academic research free of charge.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

The development of the mobile internet has spurred rapid advances in online learning, offering novel prospects for teaching and learning and a learning experience completely different from traditional instruction. Online learning harnesses the advantages of network technology and multimedia technology to transcend the boundaries of conventional education [ 1 ]. Online courses have become a popular learning mode owing to their flexibility and openness. During online learning, teachers and students are in different physical locations but interact in multiple ways (e.g., via online forum discussions and asynchronous group discussions). An analysis of online learning therefore calls for attention to students’ participation. Alqurashi [ 2 ] defined interaction in online learning as the process of constructing meaningful information and thought exchanges between more than two people; such interaction typically occurs between teachers and learners, learners and learners, and the course content and learners.

Massive open online courses (MOOCs), a 21st-century teaching mode, have greatly influenced global education. Data released by China’s Ministry of Education in 2020 show that the country ranks first globally in the number and scale of higher education MOOCs. The COVID-19 outbreak has further propelled this learning mode, with universities being urged to leverage MOOCs and other online resource platforms to respond to government’s “School’s Out, But Class’s On” policy [ 3 ]. Besides MOOCs, to reduce in-person gatherings and curb the spread of COVID-19, various online learning methods have since become ubiquitous [ 4 ]. Though Lederman asserted that the COVID-19 outbreak has positioned online learning technologies as the best way for teachers and students to obtain satisfactory learning experiences [ 5 ], it remains unclear whether the COVID-19 pandemic has encouraged interaction in online learning, as interactions between students and others play key roles in academic performance and largely determine the quality of learning experiences [ 6 ]. Similarly, it is also unclear what impact the COVID-19 pandemic has had on the scale of online learning.

Social constructivism paints learning as a social phenomenon. As such, analyzing the social structures or patterns that emerge during the learning process can shed light on learning-based interaction [ 7 ]. Social network analysis helps to explain how a social network, rooted in interactions between learners and their peers, guides individuals’ behavior, emotions, and outcomes. This analytical approach is especially useful for evaluating interactive relationships between network members [ 8 ]. Mohammed cited social network analysis (SNA) as a method that can provide timely information about students, learning communities and interactive networks. SNA has been applied in numerous fields, including education, to identify the number and characteristics of interelement relationships. For example, Lee et al. also used SNA to explore the effects of blogs on peer relationships [ 7 ]. Therefore, adopting SNA to examine interactions in online learning communities during the COVID-19 pandemic can uncover potential issues with this online learning model.

Taking China’s icourse.163 MOOC platform as an example, we chose 15 courses with a large number of participants for SNA, focusing on learners’ interaction characteristics before, during, and after the COVID-19 outbreak. We visually assessed changes in the scale of network interaction before, during, and after the outbreak along with the characteristics of interaction in Gephi. Examining students’ interactions in different courses revealed distinct interactive network characteristics, the pandemic’s impact on online courses, and relevant suggestions. Findings are expected to promote effective interaction and deep learning among students in addition to serving as a reference for the development of other online learning communities.

2. Literature review and research questions

Interaction is deemed as central to the educational experience and is a major focus of research on online learning. Moore began to study the problem of interaction in distance education as early as 1989. He defined three core types of interaction: student–teacher, student–content, and student–student [ 9 ]. Lear et al. [ 10 ] described an interactivity/ community-process model of distance education: they specifically discussed the relationships between interactivity, community awareness, and engaging learners and found interactivity and community awareness to be correlated with learner engagement. Zulfikar et al. [ 11 ] suggested that discussions initiated by the students encourage more students’ engagement than discussions initiated by the instructors. It is most important to afford learners opportunities to interact purposefully with teachers, and improving the quality of learner interaction is crucial to fostering profound learning [ 12 ]. Interaction is an important way for learners to communicate and share information, and a key factor in the quality of online learning [ 13 ].

Timely feedback is the main component of online learning interaction. Woo and Reeves discovered that students often become frustrated when they fail to receive prompt feedback [ 14 ]. Shelley et al. conducted a three-year study of graduate and undergraduate students’ satisfaction with online learning at universities and found that interaction with educators and students is the main factor affecting satisfaction [ 15 ]. Teachers therefore need to provide students with scoring justification, support, and constructive criticism during online learning. Some researchers examined online learning during the COVID-19 pandemic. They found that most students preferred face-to-face learning rather than online learning due to obstacles faced online, such as a lack of motivation, limited teacher-student interaction, and a sense of isolation when learning in different times and spaces [ 16 , 17 ]. However, it can be reduced by enhancing the online interaction between teachers and students [ 18 ].

Research showed that interactions contributed to maintaining students’ motivation to continue learning [ 19 ]. Baber argued that interaction played a key role in students’ academic performance and influenced the quality of the online learning experience [ 20 ]. Hodges et al. maintained that well-designed online instruction can lead to unique teaching experiences [ 21 ]. Banna et al. mentioned that using discussion boards, chat sessions, blogs, wikis, and other tools could promote student interaction and improve participation in online courses [ 22 ]. During the COVID-19 pandemic, Mahmood proposed a series of teaching strategies suitable for distance learning to improve its effectiveness [ 23 ]. Lapitan et al. devised an online strategy to ease the transition from traditional face-to-face instruction to online learning [ 24 ]. The preceding discussion suggests that online learning goes beyond simply providing learning resources; teachers should ideally design real-life activities to give learners more opportunities to participate.

As mentioned, COVID-19 has driven many scholars to explore the online learning environment. However, most have ignored the uniqueness of online learning during this time and have rarely compared pre- and post-pandemic online learning interaction. Taking China’s icourse.163 MOOC platform as an example, we chose 15 courses with a large number of participants for SNA, centering on student interaction before and after the pandemic. Gephi was used to visually analyze changes in the scale and characteristics of network interaction. The following questions were of particular interest:

  • (1) Can the COVID-19 pandemic promote the expansion of online learning?
  • (2a) What are the characteristics of online learning interaction during the pandemic?
  • (2b) What are the characteristics of online learning interaction after the pandemic?
  • (3) How do interaction characteristics differ between social science courses and natural science courses?

3. Methodology

3.1 research context.

We selected several courses with a large number of participants and extensive online interaction among hundreds of courses on the icourse.163 MOOC platform. These courses had been offered on the platform for at least three semesters, covering three periods (i.e., before, during, and after the COVID-19 outbreak). To eliminate the effects of shifts in irrelevant variables (e.g., course teaching activities), we chose several courses with similar teaching activities and compared them on multiple dimensions. All course content was taught online. The teachers of each course posted discussion threads related to learning topics; students were expected to reply via comments. Learners could exchange ideas freely in their responses in addition to asking questions and sharing their learning experiences. Teachers could answer students’ questions as well. Conversations in the comment area could partly compensate for a relative absence of online classroom interaction. Teacher–student interaction is conducive to the formation of a social network structure and enabled us to examine teachers’ and students’ learning behavior through SNA. The comment areas in these courses were intended for learners to construct knowledge via reciprocal communication. Meanwhile, by answering students’ questions, teachers could encourage them to reflect on their learning progress. These courses’ successive terms also spanned several phases of COVID-19, allowing us to ascertain the pandemic’s impact on online learning.

3.2 Data collection and preprocessing

To avoid interference from invalid or unclear data, the following criteria were applied to select representative courses: (1) generality (i.e., public courses and professional courses were chosen from different schools across China); (2) time validity (i.e., courses were held before during, and after the pandemic); and (3) notability (i.e., each course had at least 2,000 participants). We ultimately chose 15 courses across the social sciences and natural sciences (see Table 1 ). The coding is used to represent the course name.

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To discern courses’ evolution during the pandemic, we gathered data on three terms before, during, and after the COVID-19 outbreak in addition to obtaining data from two terms completed well before the pandemic and long after. Our final dataset comprised five sets of interactive data. Finally, we collected about 120,000 comments for SNA. Because each course had a different start time—in line with fluctuations in the number of confirmed COVID-19 cases in China and the opening dates of most colleges and universities—we divided our sample into five phases: well before the pandemic (Phase I); before the pandemic (Phase Ⅱ); during the pandemic (Phase Ⅲ); after the pandemic (Phase Ⅳ); and long after the pandemic (Phase Ⅴ). We sought to preserve consistent time spans to balance the amount of data in each period ( Fig 1 ).

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3.3 Instrumentation

Participants’ comments and “thumbs-up” behavior data were converted into a network structure and compared using social network analysis (SNA). Network analysis, according to M’Chirgui, is an effective tool for clarifying network relationships by employing sophisticated techniques [ 25 ]. Specifically, SNA can help explain the underlying relationships among team members and provide a better understanding of their internal processes. Yang and Tang used SNA to discuss the relationship between team structure and team performance [ 26 ]. Golbeck argued that SNA could improve the understanding of students’ learning processes and reveal learners’ and teachers’ role dynamics [ 27 ].

To analyze Question (1), the number of nodes and diameter in the generated network were deemed as indicators of changes in network size. Social networks are typically represented as graphs with nodes and degrees, and node count indicates the sample size [ 15 ]. Wellman et al. proposed that the larger the network scale, the greater the number of network members providing emotional support, goods, services, and companionship [ 28 ]. Jan’s study measured the network size by counting the nodes which represented students, lecturers, and tutors [ 29 ]. Similarly, network nodes in the present study indicated how many learners and teachers participated in the course, with more nodes indicating more participants. Furthermore, we investigated the network diameter, a structural feature of social networks, which is a common metric for measuring network size in SNA [ 30 ]. The network diameter refers to the longest path between any two nodes in the network. There has been evidence that a larger network diameter leads to greater spread of behavior [ 31 ]. Likewise, Gašević et al. found that larger networks were more likely to spread innovative ideas about educational technology when analyzing MOOC-related research citations [ 32 ]. Therefore, we employed node count and network diameter to measure the network’s spatial size and further explore the expansion characteristic of online courses. Brief introduction of these indicators can be summarized in Table 2 .

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To address Question (2), a list of interactive analysis metrics in SNA were introduced to scrutinize learners’ interaction characteristics in online learning during and after the pandemic, as shown below:

  • (1) The average degree reflects the density of the network by calculating the average number of connections for each node. As Rong and Xu suggested, the average degree of a network indicates how active its participants are [ 33 ]. According to Hu, a higher average degree implies that more students are interacting directly with each other in a learning context [ 34 ]. The present study inherited the concept of the average degree from these previous studies: the higher the average degree, the more frequent the interaction between individuals in the network.
  • (2) Essentially, a weighted average degree in a network is calculated by multiplying each degree by its respective weight, and then taking the average. Bydžovská took the strength of the relationship into account when determining the weighted average degree [ 35 ]. By calculating friendship’s weighted value, Maroulis assessed peer achievement within a small-school reform [ 36 ]. Accordingly, we considered the number of interactions as the weight of the degree, with a higher average degree indicating more active interaction among learners.
  • (3) Network density is the ratio between actual connections and potential connections in a network. The more connections group members have with each other, the higher the network density. In SNA, network density is similar to group cohesion, i.e., a network of more strong relationships is more cohesive [ 37 ]. Network density also reflects how much all members are connected together [ 38 ]. Therefore, we adopted network density to indicate the closeness among network members. Higher network density indicates more frequent interaction and closer communication among students.
  • (4) Clustering coefficient describes local network attributes and indicates that two nodes in the network could be connected through adjacent nodes. The clustering coefficient measures users’ tendency to gather (cluster) with others in the network: the higher the clustering coefficient, the more frequently users communicate with other group members. We regarded this indicator as a reflection of the cohesiveness of the group [ 39 ].
  • (5) In a network, the average path length is the average number of steps along the shortest paths between any two nodes. Oliveres has observed that when an average path length is small, the route from one node to another is shorter when graphed [ 40 ]. This is especially true in educational settings where students tend to become closer friends. So we consider that the smaller the average path length, the greater the possibility of interaction between individuals in the network.
  • (6) A network with a large number of nodes, but whose average path length is surprisingly small, is known as the small-world effect [ 41 ]. A higher clustering coefficient and shorter average path length are important indicators of a small-world network: a shorter average path length enables the network to spread information faster and more accurately; a higher clustering coefficient can promote frequent knowledge exchange within the group while boosting the timeliness and accuracy of knowledge dissemination [ 42 ]. Brief introduction of these indicators can be summarized in Table 3 .

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To analyze Question 3, we used the concept of closeness centrality, which determines how close a vertex is to others in the network. As Opsahl et al. explained, closeness centrality reveals how closely actors are coupled with their entire social network [ 43 ]. In order to analyze social network-based engineering education, Putnik et al. examined closeness centrality and found that it was significantly correlated with grades [ 38 ]. We used closeness centrality to measure the position of an individual in the network. Brief introduction of these indicators can be summarized in Table 4 .

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3.4 Ethics statement

This study was approved by the Academic Committee Office (ACO) of South China Normal University ( http://fzghb.scnu.edu.cn/ ), Guangzhou, China. Research data were collected from the open platform and analyzed anonymously. There are thus no privacy issues involved in this study.

4.1 COVID-19’s role in promoting the scale of online courses was not as important as expected

As shown in Fig 2 , the number of course participants and nodes are closely correlated with the pandemic’s trajectory. Because the number of participants in each course varied widely, we normalized the number of participants and nodes to more conveniently visualize course trends. Fig 2 depicts changes in the chosen courses’ number of participants and nodes before the pandemic (Phase II), during the pandemic (Phase III), and after the pandemic (Phase IV). The number of participants in most courses during the pandemic exceeded those before and after the pandemic. But the number of people who participate in interaction in some courses did not increase.

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In order to better analyze the trend of interaction scale in online courses before, during, and after the pandemic, the selected courses were categorized according to their scale change. When the number of participants increased (decreased) beyond 20% (statistical experience) and the diameter also increased (decreased), the course scale was determined to have increased (decreased); otherwise, no significant change was identified in the course’s interaction scale. Courses were subsequently divided into three categories: increased interaction scale, decreased interaction scale, and no significant change. Results appear in Table 5 .

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From before the pandemic until it broke out, the interaction scale of five courses increased, accounting for 33.3% of the full sample; one course’s interaction scale declined, accounting for 6.7%. The interaction scale of nine courses decreased, accounting for 60%. The pandemic’s role in promoting online courses thus was not as important as anticipated, and most courses’ interaction scale did not change significantly throughout.

No courses displayed growing interaction scale after the pandemic: the interaction scale of nine courses fell, accounting for 60%; and the interaction scale of six courses did not shift significantly, accounting for 40%. Courses with an increased scale of interaction during the pandemic did not maintain an upward trend. On the contrary, the improvement in the pandemic caused learners’ enthusiasm for online learning to wane. We next analyzed several interaction metrics to further explore course interaction during different pandemic periods.

4.2 Characteristics of online learning interaction amid COVID-19

4.2.1 during the covid-19 pandemic, online learning interaction in some courses became more active..

Changes in course indicators with the growing interaction scale during the pandemic are presented in Fig 3 , including SS5, SS6, NS1, NS3, and NS8. The horizontal ordinate indicates the number of courses, with red color representing the rise of the indicator value on the vertical ordinate and blue representing the decline.

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Specifically: (1) The average degree and weighted average degree of the five course networks demonstrated an upward trend. The emergence of the pandemic promoted students’ enthusiasm; learners were more active in the interactive network. (2) Fig 3 shows that 3 courses had increased network density and 2 courses had decreased. The higher the network density, the more communication within the team. Even though the pandemic accelerated the interaction scale and frequency, the tightness between learners in some courses did not improve. (3) The clustering coefficient of social science courses rose whereas the clustering coefficient and small-world property of natural science courses fell. The higher the clustering coefficient and the small-world property, the better the relationship between adjacent nodes and the higher the cohesion [ 39 ]. (4) Most courses’ average path length increased as the interaction scale increased. However, when the average path length grew, adverse effects could manifest: communication between learners might be limited to a small group without multi-directional interaction.

When the pandemic emerged, the only declining network scale belonged to a natural science course (NS2). The change in each course index is pictured in Fig 4 . The abscissa indicates the size of the value, with larger values to the right. The red dot indicates the index value before the pandemic; the blue dot indicates its value during the pandemic. If the blue dot is to the right of the red dot, then the value of the index increased; otherwise, the index value declined. Only the weighted average degree of the course network increased. The average degree, network density decreased, indicating that network members were not active and that learners’ interaction degree and communication frequency lessened. Despite reduced learner interaction, the average path length was small and the connectivity between learners was adequate.

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4.2.2 After the COVID-19 pandemic, the scale decreased rapidly, but most course interaction was more effective.

Fig 5 shows the changes in various courses’ interaction indicators after the pandemic, including SS1, SS2, SS3, SS6, SS7, NS2, NS3, NS7, and NS8.

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Specifically: (1) The average degree and weighted average degree of most course networks decreased. The scope and intensity of interaction among network members declined rapidly, as did learners’ enthusiasm for communication. (2) The network density of seven courses also fell, indicating weaker connections between learners in most courses. (3) In addition, the clustering coefficient and small-world property of most course networks decreased, suggesting little possibility of small groups in the network. The scope of interaction between learners was not limited to a specific space, and the interaction objects had no significant tendencies. (4) Although the scale of course interaction became smaller in this phase, the average path length of members’ social networks shortened in nine courses. Its shorter average path length would expedite the spread of information within the network as well as communication and sharing among network members.

Fig 6 displays the evolution of course interaction indicators without significant changes in interaction scale after the pandemic, including SS4, SS5, NS1, NS4, NS5, and NS6.

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Specifically: (1) Some course members’ social networks exhibited an increase in the average and weighted average. In these cases, even though the course network’s scale did not continue to increase, communication among network members rose and interaction became more frequent and deeper than before. (2) Network density and average path length are indicators of social network density. The greater the network density, the denser the social network; the shorter the average path length, the more concentrated the communication among network members. However, at this phase, the average path length and network density in most courses had increased. Yet the network density remained small despite having risen ( Table 6 ). Even with more frequent learner interaction, connections remained distant and the social network was comparatively sparse.

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In summary, the scale of interaction did not change significantly overall. Nonetheless, some course members’ frequency and extent of interaction increased, and the relationships between network members became closer as well. In the study, we found it interesting that the interaction scale of Economics (a social science course) course and Electrodynamics (a natural science course) course expanded rapidly during the pandemic and retained their interaction scale thereafter. We next assessed these two courses to determine whether their level of interaction persisted after the pandemic.

4.3 Analyses of natural science courses and social science courses

4.3.1 analyses of the interaction characteristics of economics and electrodynamics..

Economics and Electrodynamics are social science courses and natural science courses, respectively. Members’ interaction within these courses was similar: the interaction scale increased significantly when COVID-19 broke out (Phase Ⅲ), and no significant changes emerged after the pandemic (Phase Ⅴ). We hence focused on course interaction long after the outbreak (Phase V) and compared changes across multiple indicators, as listed in Table 7 .

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As the pandemic continued to improve, the number of participants and the diameter long after the outbreak (Phase V) each declined for Economics compared with after the pandemic (Phase IV). The interaction scale decreased, but the interaction between learners was much deeper. Specifically: (1) The weighted average degree, network density, clustering coefficient, and small-world property each reflected upward trends. The pandemic therefore exerted a strong impact on this course. Interaction was well maintained even after the pandemic. The smaller network scale promoted members’ interaction and communication. (2) Compared with after the pandemic (Phase IV), members’ network density increased significantly, showing that relationships between learners were closer and that cohesion was improving. (3) At the same time, as the clustering coefficient and small-world property grew, network members demonstrated strong small-group characteristics: the communication between them was deepening and their enthusiasm for interaction was higher. (4) Long after the COVID-19 outbreak (Phase V), the average path length was reduced compared with previous terms, knowledge flowed more quickly among network members, and the degree of interaction gradually deepened.

The average degree, weighted average degree, network density, clustering coefficient, and small-world property of Electrodynamics all decreased long after the COVID-19 outbreak (Phase V) and were lower than during the outbreak (Phase Ⅲ). The level of learner interaction therefore gradually declined long after the outbreak (Phase V), and connections between learners were no longer active. Although the pandemic increased course members’ extent of interaction, this rise was merely temporary: students’ enthusiasm for learning waned rapidly and their interaction decreased after the pandemic (Phase IV). To further analyze the interaction characteristics of course members in Economics and Electrodynamics, we evaluated the closeness centrality of their social networks, as shown in section 4.3.2.

4.3.2 Analysis of the closeness centrality of Economics and Electrodynamics.

The change in the closeness centrality of social networks in Economics was small, and no sharp upward trend appeared during the pandemic outbreak, as shown in Fig 7 . The emergence of COVID-19 apparently fostered learners’ interaction in Economics albeit without a significant impact. The closeness centrality changed in Electrodynamics varied from that of Economics: upon the COVID-19 outbreak, closeness centrality was significantly different from other semesters. Communication between learners was closer and interaction was more effective. Electrodynamics course members’ social network proximity decreased rapidly after the pandemic. Learners’ communication lessened. In general, Economics course showed better interaction before the outbreak and was less affected by the pandemic; Electrodynamics course was more affected by the pandemic and showed different interaction characteristics at different periods of the pandemic.

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(Note: "****" indicates the significant distinction in closeness centrality between the two periods, otherwise no significant distinction).

https://doi.org/10.1371/journal.pone.0273016.g007

5. Discussion

We referred to discussion forums from several courses on the icourse.163 MOOC platform to compare online learning before, during, and after the COVID-19 pandemic via SNA and to delineate the pandemic’s effects on online courses. Only 33.3% of courses in our sample increased in terms of interaction during the pandemic; the scale of interaction did not rise in any courses thereafter. When the courses scale rose, the scope and frequency of interaction showed upward trends during the pandemic; and the clustering coefficient of natural science courses and social science courses differed: the coefficient for social science courses tended to rise whereas that for natural science courses generally declined. When the pandemic broke out, the interaction scale of a single natural science course decreased along with its interaction scope and frequency. The amount of interaction in most courses shrank rapidly during the pandemic and network members were not as active as they had been before. However, after the pandemic, some courses saw declining interaction but greater communication between members; interaction also became more frequent and deeper than before.

5.1 During the COVID-19 pandemic, the scale of interaction increased in only a few courses

The pandemic outbreak led to a rapid increase in the number of participants in most courses; however, the change in network scale was not significant. The scale of online interaction expanded swiftly in only a few courses; in others, the scale either did not change significantly or displayed a downward trend. After the pandemic, the interaction scale in most courses decreased quickly; the same pattern applied to communication between network members. Learners’ enthusiasm for online interaction reduced as the circumstances of the pandemic improved—potentially because, during the pandemic, China’s Ministry of Education declared “School’s Out, But Class’s On” policy. Major colleges and universities were encouraged to use the Internet and informational resources to provide learning support, hence the sudden increase in the number of participants and interaction in online courses [ 46 ]. After the pandemic, students’ enthusiasm for online learning gradually weakened, presumably due to easing of the pandemic [ 47 ]. More activities also transitioned from online to offline, which tempered learners’ online discussion. Research has shown that long-term online learning can even bore students [ 48 ].

Most courses’ interaction scale decreased significantly after the pandemic. First, teachers and students occupied separate spaces during the outbreak, had few opportunities for mutual cooperation and friendship, and lacked a sense of belonging [ 49 ]. Students’ enthusiasm for learning dissipated over time [ 50 ]. Second, some teachers were especially concerned about adapting in-person instructional materials for digital platforms; their pedagogical methods were ineffective, and they did not provide learning activities germane to student interaction [ 51 ]. Third, although teachers and students in remote areas were actively engaged in online learning, some students could not continue to participate in distance learning due to inadequate technology later in the outbreak [ 52 ].

5.2 Characteristics of online learning interaction during and after the COVID-19 pandemic

5.2.1 during the covid-19 pandemic, online interaction in most courses did not change significantly..

The interaction scale of only a few courses increased during the pandemic. The interaction scope and frequency of these courses climbed as well. Yet even as the degree of network interaction rose, course network density did not expand in all cases. The pandemic sparked a surge in the number of online learners and a rapid increase in network scale, but students found it difficult to interact with all learners. Yau pointed out that a greater network scale did not enrich the range of interaction between individuals; rather, the number of individuals who could interact directly was limited [ 53 ]. The internet facilitates interpersonal communication. However, not everyone has the time or ability to establish close ties with others [ 54 ].

In addition, social science courses and natural science courses in our sample revealed disparate trends in this regard: the clustering coefficient of social science courses increased and that of natural science courses decreased. Social science courses usually employ learning approaches distinct from those in natural science courses [ 55 ]. Social science courses emphasize critical and innovative thinking along with personal expression [ 56 ]. Natural science courses focus on practical skills, methods, and principles [ 57 ]. Therefore, the content of social science courses can spur large-scale discussion among learners. Some course evaluations indicated that the course content design was suboptimal as well: teachers paid close attention to knowledge transmission and much less to piquing students’ interest in learning. In addition, the thread topics that teachers posted were scarcely diversified and teachers’ questions lacked openness. These attributes could not spark active discussion among learners.

5.2.2 Online learning interaction declined after the COVID-19 pandemic.

Most courses’ interaction scale and intensity decreased rapidly after the pandemic, but some did not change. Courses with a larger network scale did not continue to expand after the outbreak, and students’ enthusiasm for learning paled. The pandemic’s reduced severity also influenced the number of participants in online courses. Meanwhile, restored school order moved many learning activities from virtual to in-person spaces. Face-to-face learning has gradually replaced online learning, resulting in lower enrollment and less interaction in online courses. Prolonged online courses could have also led students to feel lonely and to lack a sense of belonging [ 58 ].

The scale of interaction in some courses did not change substantially after the pandemic yet learners’ connections became tighter. We hence recommend that teachers seize pandemic-related opportunities to design suitable activities. Additionally, instructors should promote student-teacher and student-student interaction, encourage students to actively participate online, and generally intensify the impact of online learning.

5.3 What are the characteristics of interaction in social science courses and natural science courses?

The level of interaction in Economics (a social science course) was significantly higher than that in Electrodynamics (a natural science course), and the small-world property in Economics increased as well. To boost online courses’ learning-related impacts, teachers can divide groups of learners based on the clustering coefficient and the average path length. Small groups of students may benefit teachers in several ways: to participate actively in activities intended to expand students’ knowledge, and to serve as key actors in these small groups. Cultivating students’ keenness to participate in class activities and self-management can also help teachers guide learner interaction and foster deep knowledge construction.

As evidenced by comments posted in the Electrodynamics course, we observed less interaction between students. Teachers also rarely urged students to contribute to conversations. These trends may have arisen because teachers and students were in different spaces. Teachers might have struggled to discern students’ interaction status. Teachers could also have failed to intervene in time, to design online learning activities that piqued learners’ interest, and to employ sound interactive theme planning and guidance. Teachers are often active in traditional classroom settings. Their roles are comparatively weakened online, such that they possess less control over instruction [ 59 ]. Online instruction also requires a stronger hand in learning: teachers should play a leading role in regulating network members’ interactive communication [ 60 ]. Teachers can guide learners to participate, help learners establish social networks, and heighten students’ interest in learning [ 61 ]. Teachers should attend to core members in online learning while also considering edge members; by doing so, all network members can be driven to share their knowledge and become more engaged. Finally, teachers and assistant teachers should help learners develop knowledge, exchange topic-related ideas, pose relevant questions during course discussions, and craft activities that enable learners to interact online [ 62 ]. These tactics can improve the effectiveness of online learning.

As described, network members displayed distinct interaction behavior in Economics and Electrodynamics courses. First, these courses varied in their difficulty: the social science course seemed easier to understand and focused on divergent thinking. Learners were often willing to express their views in comments and to ponder others’ perspectives [ 63 ]. The natural science course seemed more demanding and was oriented around logical thinking and skills [ 64 ]. Second, courses’ content differed. In general, social science courses favor the acquisition of declarative knowledge and creative knowledge compared with natural science courses. Social science courses also entertain open questions [ 65 ]. Natural science courses revolve around principle knowledge, strategic knowledge, and transfer knowledge [ 66 ]. Problems in these courses are normally more complicated than those in social science courses. Third, the indicators affecting students’ attitudes toward learning were unique. Guo et al. discovered that “teacher feedback” most strongly influenced students’ attitudes towards learning social science courses but had less impact on students in natural science courses [ 67 ]. Therefore, learners in social science courses likely expect more feedback from teachers and greater interaction with others.

6. Conclusion and future work

Our findings show that the network interaction scale of some online courses expanded during the COVID-19 pandemic. The network scale of most courses did not change significantly, demonstrating that the pandemic did not notably alter the scale of course interaction. Online learning interaction among course network members whose interaction scale increased also became more frequent during the pandemic. Once the outbreak was under control, although the scale of interaction declined, the level and scope of some courses’ interactive networks continued to rise; interaction was thus particularly effective in these cases. Overall, the pandemic appeared to have a relatively positive impact on online learning interaction. We considered a pair of courses in detail and found that Economics (a social science course) fared much better than Electrodynamics (a natural science course) in classroom interaction; learners were more willing to partake in-class activities, perhaps due to these courses’ unique characteristics. Brint et al. also came to similar conclusions [ 57 ].

This study was intended to be rigorous. Even so, several constraints can be addressed in future work. The first limitation involves our sample: we focused on a select set of courses hosted on China’s icourse.163 MOOC platform. Future studies should involve an expansive collection of courses to provide a more holistic understanding of how the pandemic has influenced online interaction. Second, we only explored the interactive relationship between learners and did not analyze interactive content. More in-depth content analysis should be carried out in subsequent research. All in all, the emergence of COVID-19 has provided a new path for online learning and has reshaped the distance learning landscape. To cope with associated challenges, educational practitioners will need to continue innovating in online instructional design, strengthen related pedagogy, optimize online learning conditions, and bolster teachers’ and students’ competence in online learning.

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The pandemic has had devastating impacts on learning. What will it take to help students catch up?

Subscribe to the brown center on education policy newsletter, megan kuhfeld , megan kuhfeld senior research scientist - nwea @megankuhfeld jim soland , jim soland assistant professor, school of education and human development - university of virginia, affiliated research fellow - nwea @jsoland karyn lewis , and karyn lewis director, center for school and student progress - nwea @karynlew emily morton emily morton research scientist - nwea @emily_r_morton.

March 3, 2022

As we reach the two-year mark of the initial wave of pandemic-induced school shutdowns, academic normalcy remains out of reach for many students, educators, and parents. In addition to surging COVID-19 cases at the end of 2021, schools have faced severe staff shortages , high rates of absenteeism and quarantines , and rolling school closures . Furthermore, students and educators continue to struggle with mental health challenges , higher rates of violence and misbehavior , and concerns about lost instructional time .

As we outline in our new research study released in January, the cumulative impact of the COVID-19 pandemic on students’ academic achievement has been large. We tracked changes in math and reading test scores across the first two years of the pandemic using data from 5.4 million U.S. students in grades 3-8. We focused on test scores from immediately before the pandemic (fall 2019), following the initial onset (fall 2020), and more than one year into pandemic disruptions (fall 2021).

Average fall 2021 math test scores in grades 3-8 were 0.20-0.27 standard deviations (SDs) lower relative to same-grade peers in fall 2019, while reading test scores were 0.09-0.18 SDs lower. This is a sizable drop. For context, the math drops are significantly larger than estimated impacts from other large-scale school disruptions, such as after Hurricane Katrina—math scores dropped 0.17 SDs in one year for New Orleans evacuees .

Even more concerning, test-score gaps between students in low-poverty and high-poverty elementary schools grew by approximately 20% in math (corresponding to 0.20 SDs) and 15% in reading (0.13 SDs), primarily during the 2020-21 school year. Further, achievement tended to drop more between fall 2020 and 2021 than between fall 2019 and 2020 (both overall and differentially by school poverty), indicating that disruptions to learning have continued to negatively impact students well past the initial hits following the spring 2020 school closures.

These numbers are alarming and potentially demoralizing, especially given the heroic efforts of students to learn and educators to teach in incredibly trying times. From our perspective, these test-score drops in no way indicate that these students represent a “ lost generation ” or that we should give up hope. Most of us have never lived through a pandemic, and there is so much we don’t know about students’ capacity for resiliency in these circumstances and what a timeline for recovery will look like. Nor are we suggesting that teachers are somehow at fault given the achievement drops that occurred between 2020 and 2021; rather, educators had difficult jobs before the pandemic, and now are contending with huge new challenges, many outside their control.

Clearly, however, there’s work to do. School districts and states are currently making important decisions about which interventions and strategies to implement to mitigate the learning declines during the last two years. Elementary and Secondary School Emergency Relief (ESSER) investments from the American Rescue Plan provided nearly $200 billion to public schools to spend on COVID-19-related needs. Of that sum, $22 billion is dedicated specifically to addressing learning loss using “evidence-based interventions” focused on the “ disproportionate impact of COVID-19 on underrepresented student subgroups. ” Reviews of district and state spending plans (see Future Ed , EduRecoveryHub , and RAND’s American School District Panel for more details) indicate that districts are spending their ESSER dollars designated for academic recovery on a wide variety of strategies, with summer learning, tutoring, after-school programs, and extended school-day and school-year initiatives rising to the top.

Comparing the negative impacts from learning disruptions to the positive impacts from interventions

To help contextualize the magnitude of the impacts of COVID-19, we situate test-score drops during the pandemic relative to the test-score gains associated with common interventions being employed by districts as part of pandemic recovery efforts. If we assume that such interventions will continue to be as successful in a COVID-19 school environment, can we expect that these strategies will be effective enough to help students catch up? To answer this question, we draw from recent reviews of research on high-dosage tutoring , summer learning programs , reductions in class size , and extending the school day (specifically for literacy instruction) . We report effect sizes for each intervention specific to a grade span and subject wherever possible (e.g., tutoring has been found to have larger effects in elementary math than in reading).

Figure 1 shows the standardized drops in math test scores between students testing in fall 2019 and fall 2021 (separately by elementary and middle school grades) relative to the average effect size of various educational interventions. The average effect size for math tutoring matches or exceeds the average COVID-19 score drop in math. Research on tutoring indicates that it often works best in younger grades, and when provided by a teacher rather than, say, a parent. Further, some of the tutoring programs that produce the biggest effects can be quite intensive (and likely expensive), including having full-time tutors supporting all students (not just those needing remediation) in one-on-one settings during the school day. Meanwhile, the average effect of reducing class size is negative but not significant, with high variability in the impact across different studies. Summer programs in math have been found to be effective (average effect size of .10 SDs), though these programs in isolation likely would not eliminate the COVID-19 test-score drops.

Figure 1: Math COVID-19 test-score drops compared to the effect sizes of various educational interventions

Figure 1 – Math COVID-19 test-score drops compared to the effect sizes of various educational interventions

Source: COVID-19 score drops are pulled from Kuhfeld et al. (2022) Table 5; reduction-in-class-size results are from pg. 10 of Figles et al. (2018) Table 2; summer program results are pulled from Lynch et al (2021) Table 2; and tutoring estimates are pulled from Nictow et al (2020) Table 3B. Ninety-five percent confidence intervals are shown with vertical lines on each bar.

Notes: Kuhfeld et al. and Nictow et al. reported effect sizes separately by grade span; Figles et al. and Lynch et al. report an overall effect size across elementary and middle grades. We were unable to find a rigorous study that reported effect sizes for extending the school day/year on math performance. Nictow et al. and Kraft & Falken (2021) also note large variations in tutoring effects depending on the type of tutor, with larger effects for teacher and paraprofessional tutoring programs than for nonprofessional and parent tutoring. Class-size reductions included in the Figles meta-analysis ranged from a minimum of one to minimum of eight students per class.

Figure 2 displays a similar comparison using effect sizes from reading interventions. The average effect of tutoring programs on reading achievement is larger than the effects found for the other interventions, though summer reading programs and class size reduction both produced average effect sizes in the ballpark of the COVID-19 reading score drops.

Figure 2: Reading COVID-19 test-score drops compared to the effect sizes of various educational interventions

Figure 2 – Reading COVID-19 test-score drops compared to the effect sizes of various educational interventions

Source: COVID-19 score drops are pulled from Kuhfeld et al. (2022) Table 5; extended-school-day results are from Figlio et al. (2018) Table 2; reduction-in-class-size results are from pg. 10 of Figles et al. (2018) ; summer program results are pulled from Kim & Quinn (2013) Table 3; and tutoring estimates are pulled from Nictow et al (2020) Table 3B. Ninety-five percent confidence intervals are shown with vertical lines on each bar.

Notes: While Kuhfeld et al. and Nictow et al. reported effect sizes separately by grade span, Figlio et al. and Kim & Quinn report an overall effect size across elementary and middle grades. Class-size reductions included in the Figles meta-analysis ranged from a minimum of one to minimum of eight students per class.

There are some limitations of drawing on research conducted prior to the pandemic to understand our ability to address the COVID-19 test-score drops. First, these studies were conducted under conditions that are very different from what schools currently face, and it is an open question whether the effectiveness of these interventions during the pandemic will be as consistent as they were before the pandemic. Second, we have little evidence and guidance about the efficacy of these interventions at the unprecedented scale that they are now being considered. For example, many school districts are expanding summer learning programs, but school districts have struggled to find staff interested in teaching summer school to meet the increased demand. Finally, given the widening test-score gaps between low- and high-poverty schools, it’s uncertain whether these interventions can actually combat the range of new challenges educators are facing in order to narrow these gaps. That is, students could catch up overall, yet the pandemic might still have lasting, negative effects on educational equality in this country.

Given that the current initiatives are unlikely to be implemented consistently across (and sometimes within) districts, timely feedback on the effects of initiatives and any needed adjustments will be crucial to districts’ success. The Road to COVID Recovery project and the National Student Support Accelerator are two such large-scale evaluation studies that aim to produce this type of evidence while providing resources for districts to track and evaluate their own programming. Additionally, a growing number of resources have been produced with recommendations on how to best implement recovery programs, including scaling up tutoring , summer learning programs , and expanded learning time .

Ultimately, there is much work to be done, and the challenges for students, educators, and parents are considerable. But this may be a moment when decades of educational reform, intervention, and research pay off. Relying on what we have learned could show the way forward.

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How COVID-19 caused a global learning crisis

Executive summary.

In our latest report on unfinished learning, we examine the impact of the COVID-19 pandemic on student learning and well-being, and identify potential considerations for school systems as they support students in recovery and beyond. Our key findings include the following:

  • The length of school closures varied widely across the world. School buildings in middle-income Latin America and South Asia were fully or partially closed the longest—for 75 weeks or more. Those in high-income Europe and Central Asia were fully or partially closed for less time (30 weeks on average), as were those in low-income sub-Saharan Africa (34 weeks on average).

About the authors

This article is a collaborative effort by Jake Bryant , Felipe Child , Emma Dorn , Jose Espinosa, Stephen Hall , Topsy Kola-Oyeneyin , Cheryl Lim, Frédéric Panier, Jimmy Sarakatsannis , Dirk Schmautzer , Seckin Ungur , and Bart Woord, representing views from McKinsey’s Education Practice.

  • Access to quality remote and hybrid learning also varied both across and within countries. In Tanzania, while school buildings were closed, children in just 6 percent of households listened to radio lessons, 5 percent accessed TV lessons, and fewer than 1 percent participated in online learning. 1 Jacobus Cilliers and Shardul Oza, “What did children do during school closures? Insights from a parent survey in Tanzania,” Research on Improving Systems of Education (RISE), May 19, 2021.
  • Furthermore, pandemic-related learning delays stack up on top of historical learning inequities. The World Bank estimates that while students in high-income countries gained an average of 50 harmonized learning outcomes (HLO) points a year prepandemic, students in low-income countries were gaining just 20, leaving those students several years behind. 2 Noam Angrist et al., “Measuring human capital using global learning data,” Nature , March 2021, Volume 592.
  • High-performing systems, with relatively high levels of pre-COVID-19 performance, where students may be about one to five months behind due to the pandemic (for example, North America and Europe, where students are, on average, four months behind).
  • Low-income prepandemic-challenged systems, with very low levels of pre-COVID-19 learning, where students may be about three to eight months behind due to the pandemic (for example, sub-Saharan Africa, where students are on average six months behind).
  • Pandemic-affected middle-income systems, with moderate levels of pre-COVID-19 learning, where students may be nine to 15 months behind (for example, Latin America and South Asia, where students are, on average, 12 months behind).
  • The pandemic also increased inequalities within systems. For example, it widened gaps between majority Black and majority White schools in the United States and increased preexisting urban-rural divides in Ethiopia.
  • Beyond learning, the pandemic has had broader social and emotional impacts on students globally—with rising mental-health concerns, reports of violence against children, rising obesity, increases in teenage pregnancy, and rising levels of chronic absenteeism and dropouts.
  • Lower levels of learning translate into lower future earnings potential for students and lower economic productivity for nations. By 2040, the economic impact of pandemic-related learning delays could lead to annual losses of $1.6 trillion worldwide, or 0.9 percent of total global GDP.
  • Resilience: Safely reopen schools for in-person learning while ensuring resilience for future disruptions.
  • Reenrollment: Encourage students, families, and teachers to reengage with learning in effective learning environments.
  • Recovery: Support students as they recover from the academic and social-emotional impacts of the pandemic, starting with an understanding of each student’s needs.
  • Reimagining: Recommit to quality education for every child, doubling down on the fundamentals of educational excellence and innovating to adapt.

The state of global education, before and during COVID-19

In some parts of the world, students, parents, and teachers may be experiencing a novel feeling: cautious optimism. After two years of disruptions from COVID-19, the overnight shift to online and hybrid learning, and efforts to safeguard teachers, administrators, and students, cities and countries are seeing the first signs of the next normal. Masks are coming off. Events are being held in person. Extracurricular activities are back in full swing.

These signs of hope are counterbalanced by the lingering, widespread impact of the pandemic. While it’s too early to catalog all of the ways students have been affected, we are starting to see initial indications of the toll COVID-19 has taken on learning around the world. Our analysis of available data found no country was untouched, but the impact varied across regions and within countries. Even in places with effective school systems and near-universal connectivity and device access, learning delays were significant, especially for historically vulnerable populations. 3 Emma Dorn, Bryan Hancock, Jimmy Sarakatsannis, and Ellen Viruleg, “ COVID-19 and education: An emerging K-shaped recovery ,” McKinsey, December 14, 2021. In many countries that had poor education outcomes before the pandemic, the setbacks were even greater. In those countries, an even more ambitious, coordinated effort will likely be required to address the disruption students have experienced.

Our analysis highlights the extent of the challenge and demonstrates how the impact of the pandemic on learning extends across students, families, and entire communities. Beyond the direct effect on students, learning delays have the potential to affect economic growth: by 2040, according to McKinsey analysis, COVID-19-related unfinished learning could translate into $1.6 trillion in annual losses to the global economy.

Acting decisively in the near term could help to address learning delays as well as the broader social, emotional, and mental-health impact on students. In mobilizing to respond to the pandemic’s effect on student learning and thriving, countries also may need to reassess their education systems—what has been working well and what may need to be reimagined in light of the past two years. Our hope is that this article’s analysis provides a potential starting point for dialogue as nations seek to reinvigorate their education systems.

Gauging the pandemic’s widespread impact on education

One of the challenges in assessing the global effect of the pandemic on learning is the lack of data. Comparative international assessments mostly cover middle- to high-income countries and have not been carried out since the beginning of the pandemic. The next Program for International Student Assessment (PISA), for example, was delayed until 2022. 4 “PISA,” OECD, accessed March 30, 2022. Similarly, many countries had to cancel or defer national assessments. As a result, few nations have a complete data set, and many have no assessment data to indicate relative learning before and since school closures. Accordingly, our methodology used available data augmented by informed assumptions to get a directional picture of the pandemic’s effects on the scholastic achievement and well-being of students.

The pandemic’s impact on student learning

We evaluated the potential effect of the pandemic on student learning by multiplying the amount of time school was disrupted in each country by the estimated effectiveness of the schooling students received during disruptions.

The duration of school closures ran the gamut. During the 102-week period we studied (from the onset of COVID-19 to January 2022), school buildings in Latin America, including the Caribbean, and South Asia were fully or partially closed for 75 weeks or more, while those in Europe and Central Asia were fully or partially closed for an average of 30 weeks (Exhibit 1). Schools in some regions began reopening a few months into the pandemic, but as of January 2022, more than a quarter of the world’s student population resided in school systems where schools were not yet fully open.

Remote and hybrid learning similarly varied widely across and within countries. Some students were supported by internet access, devices, learning management systems, adaptive learning software, live videoconferencing with teachers and peers, and home environments with parents or hired professionals to support remote learning. Others had access to radio or television programs, paper packages, and text messaging. Some students may not have had access to any learning options. 5 What’s next? Lessons on education recovery: Findings from a survey of ministries of education amid the COVID-19 pandemic , UNESCO, UNICEF, the World Bank, and OECD, June 2021. We used the World Bank’s estimates on “mitigation effectiveness” by country income level to account for different levels of access to learning tools and quality through the pandemic (see the forthcoming methodological appendix for more details).

Our model suggests that in the first 23 months since the start of the pandemic, students around the world may have lost about eight months of learning, on average, with meaningful disparities across and within regions and countries. For example, students in South Asia, Latin America, and the Caribbean may be more than a year behind where they would have been absent the pandemic. In North America and Europe, students might be an average of four months behind (Exhibit 2).

The regional numbers only begin to tell the full story. The greater the range of school system performance and resources across regions, the greater the variation in student experiences. Students in Japan and Australia may be less than two months behind, while students in the Philippines and Indonesia may be more than a year behind where they would have been (Exhibit 3).

Within countries, the impact of COVID-19 has also affected individual students differently. Wherever assessments have taken place since the onset of the pandemic, they suggest widening gaps in both opportunity and achievement. Historically vulnerable and marginalized students are at an increased risk of falling further behind.

In the United States, students in majority Black schools were half a year behind in mathematics and reading by fall 2021, while students in majority White schools were just two months behind. 6 “ COVID-19 and education: An emerging K-shaped recovery ,” December 14, 2021. In Ethiopia, students in rural areas achieved under one-third of the expected learning from March to October 2020, while those in urban areas learned less than half of the expected amount. 7 Research on Improving Systems of Education (RISE) , “Learning inequalities widen following COVID-19 school closures in Ethiopia,” blog entry by Janice Kim, Pauline Rose, Ricardo Sabates, Dawit Tibebu Tiruneh, and Tassew Woldehanna, May 4, 2021. Assessments in New South Wales, Australia, detected minimal impact on learning overall, but third-grade students in the most disadvantaged schools experienced two months less growth in mathematics. 8 Leanne Fray et al., “The impact of COVID-19 on student learning in New South Wales primary schools: An empirical study,” The Australian Educational Researcher , 2021, Volume 48.

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Covid-19-related losses on top of historical inequalities.

The learning crisis is not new. In the years before COVID-19, many school systems faced challenges in providing learning opportunities for many of their students. The World Bank estimates that before the pandemic, more than half of students in low- and middle-income countries were living in “learning poverty”—unable to read and understand a simple text by age ten. That number may rise as high as 70 percent due to pandemic-related school disruptions. 9 Joao Azevedo et al., “The state of the global education crisis: A path to recovery,” World Bank Group, December 3, 2021.

The World Bank’s harmonized learning outcomes (HLOs) compare learning achievement and growth across countries. This measure combines multiple global student assessments into one metric, with a range of 625 for advanced attainment and 300 for minimum attainment. According to the World Bank’s 2018 HLO database, students from some countries in the Middle East, North Africa, and South Asia were several years behind their counterparts in North America and Europe before the pandemic (Exhibit 4). 10 Data Blog , “Harmonized learning outcomes: transforming learning assessment data into national education policy reforms,” blog entry by Harry A. Patrinos and Noam Angrist, August 12, 2019.

Students in these countries were also progressing more slowly each year in school. While students in high-income countries may have been gaining 50 HLO points in a year, students in low-income countries were gaining just 20. In other words, not much learning was happening in some countries even before the pandemic.

Prepandemic learning levels and pandemic-related learning delays interacted in different ways in different countries and regions. Although each country is unique, three archetypes emerge based on the performance of education systems (Exhibit 5).

High-performing systems. Countries in this archetype generally had higher pre-COVID-19 learning levels. Systems had more capacity for remote learning, and school buildings remained closed for shorter time periods. 11 “Education: From disruption to recovery,” UNESCO, accessed March 11, 2022. Data suggest that after the initial shock of the pandemic in 2020, learning delays increased only moderately with subsequent school closures in the 2021–22 school year. Some high-income countries seem to show little evidence of decreased learning overall. According to the Australian National Assessment Program–Literacy and Numeracy (NAPLAN), the COVID-19 pandemic did not have a statistically significant impact on average student literacy and numeracy levels, even in Victoria, where learning was remote for more than 120 days. 12 “Highlights from Victorian preliminary results in NAPLAN 2021,” Victoria state government, August 26, 2021; Adam Carey, Melissa Cunningham, and Anna Prytz, “‘Children have suffered enormously’: School closures leave experts divided,” The Age , Melbourne, July 25, 2021. However, in many high-income countries, the impact of the pandemic on learning remained significant. Assessments of student learning in the United States in fall 2021 suggested students had fallen four months behind in mathematics and three months behind in reading. 13 “ COVID-19 and education: An emerging K-shaped recovery ,” December 14, 2021. Inequalities in learning also increased within many of these countries, with historically marginalized students most affected.

Lower-income, prepandemic-challenged systems. This archetype consists of mostly low-income and lower-middle-income countries with very low levels of pre-COVID-19 learning. When the pandemic struck, school buildings closed for varying periods of time, 14 “Education: From disruption to recovery,” UNESCO, accessed March 11, 2022. with limited options for remote learning. In Tanzania, for example, schools were closed for 15 weeks, and during this period, just 6 percent of households reported that their children listened to radio lessons, 5 percent watched TV lessons, and fewer than 1 percent accessed educational programs on the internet. 15 Jacobus Cilliers and Shardul Oza, “What did children do during school closures? Insights from a parent survey in Tanzania,” Research on Improving Systems of Education (RISE), May 19, 2021. Across the analyzed time period, schools in sub-Saharan Africa were fully open for more weeks, on average, than schools in any other region. As a result, the pandemic’s impact on learning was relatively muted, even though many of these systems faced challenges with effective remote learning. 16 A report of six countries in Africa, for example, found limited impact of the pandemic on already-low student outcomes. For more information, see “MILO: Monitoring impacts on learning outcomes,” UNESCO, accessed March 11, 2022.

These relatively smaller pandemic learning delays are likely due in part to the limited progress students were making in schools before COVID-19. 17 World Bank blogs , “Harmonized learning outcomes: Transforming learning assessment data into national education policy reforms,” blog entry by Harry A. Patrinos and Noam Angrist, August 12, 2019. If students weren’t progressing scholastically when schools were open, closures were likely to have less impact. In Tanzania before the pandemic, three-quarters of students in grade three could not read a basic sentence. 18 “What did children do during school closures?,” May 19, 2021.

Pandemic-affected middle-income systems. School systems in Latin American and South Asian countries had low to moderate performance before COVID-19. Many middle-income countries in this group did have some capacity to plan and roll out remote-learning options, especially in urban areas. 19 “Responses to Educational Disruption Survey (REDS),” UNESCO, accessed March 11, 2022. However, pandemic-related disruptions caused widespread school closures for extended periods of time—more than 50 weeks in some countries. 20 “Education: From disruption to recovery,” UNESCO, accessed March 11, 2022. The resulting learning delays may represent a true crisis for major economies such as India, Indonesia, and Mexico, where students are more than a year behind, on average.

While some students may have just learned more slowly than they would have absent the pandemic, others in this archetype may have actually slipped backward. A study by the Azim Premji Foundation suggests that as early as January 2021, more than 90 percent of students assessed in India have lost at least one language ability (such as reading words or writing simple sentences), while more than 80 percent lost a math ability (for example, identifying single- and double-digit numbers or naming shapes). 21 Loss of learning during the pandemic , Azim Premji Foundation, February 2021. This pattern could be particularly challenging, since higher-order skills are increasingly important in middle-income countries with rising levels of workplace automation. McKinsey’s “ Jobs lost, jobs gained ” report 22 For more information, see “ Jobs lost, jobs gained: What the future of work will mean for jobs, skills, and wages ,” McKinsey Global Institute, November 28, 2017. suggests India may need 34 million to 100 million more high school graduates by 2030 to fill workplace demands. The pandemic has put existing high school graduation rates at risk, let alone the vast expansion required to meet future demand for workers.

The pandemic’s effects beyond learning

Much of the dialogue around school systems focuses on educational achievement, but schools offer more than academic instruction. A school system’s contributions may include social interaction; an opportunity for students to build relationships with caring adults; a base for extracurricular activities, from the arts to athletics; an access point for physical- and mental-health services; and a guarantee of balanced meals on a regular basis. The school year may also enable students to track their progress and celebrate milestones. When schools had to close for extended periods of time or move to hybrid learning, students were deprived of many of these benefits.

The pandemic’s impact on the social-emotional and mental and physical health of students has been measured even less than its impact on academic achievement, but early indications are concerning. Save the Children reports that 83 percent of children and 89 percent of parents globally have reported an increase in negative feelings since the pandemic began. 23 The hidden impact of COVID-19 on child protection and wellbeing , Save the Children International, September 2020. In the United States, one in three parents said they were very or extremely worried about their child’s mental health in spring 2021, with rising reported levels of student anxiety, depression, social withdrawal, and lethargy. 24 Emma Dorn, Bryan Hancock, Jimmy Sarakatsannis, and Ellen Viruleg, “ COVID-19 and education: The lingering effects of unfinished learning ,” McKinsey, July 27, 2021. Parents of Black and Hispanic students, the segments most affected by academic unfinished learning, also reported higher rates of concern about their student’s mental health and engagement with school. A UK survey found 53 percent of girls and 44 percent of boys aged 13 to 18 had experienced symptoms or trauma related to COVID-19. 25 Report1: Impact of COVID-19 on young people aged 13-24 in the UK- preliminary findings , PsyArXiv, January 20, 2021. In Bangladesh, a cross-sectional study revealed that 19.3 percent of children suffered moderate mental-health impacts, while 7.2 percent suffered from extreme mental-health effects. 26 Rajon Banik et al., “Impact of COVID-19 pandemic on the mental health of children in Bangladesh: A cross-sectional study,” Children and Youth Services Review , October 2020, Volume 117. Reports of violence against children rose in many countries. 27 “Publications,” Young Lives, accessed March 22, 2022. The pandemic affected physical health as well. Studies from the United States 28 Roger Riddell, “CDC: Child obesity jumped during COVID-19 pandemic,” K-12 Dive , September 24, 2021. and the United Kingdom 29 The annual report of Her Majesty’s chief inspector of education, children’s services and skills 2020/21 , Ofsted, December 7, 2021. show rising rates of childhood obesity. In Latin America and the Caribbean, more than 80 million children stopped receiving hot meals. 30 “We can move to online learning, but not online eating,” United Nations World Food Program, March 26, 2020. In Uganda, a record number of monthly teenage pregnancies—more than 32,000—were recorded from March 2020 to September 2021. 31 “Uganda overwhelmed by 32,000 monthly teen pregnancies,” Yeni Şafak , December 12, 2021.

Some students may never return to formal schooling at all. Even in high-income systems, levels of chronic absenteeism are rising, and some students have not reengaged in school. In the United States, 1.7 million to 3.3 million eighth to 12th graders may drop out of school because of the pandemic. In low- and middle-income countries, the situation could be far worse. Up to one-third of Ugandan students may not return to the classroom. This pattern is in line with past historical crises involving school closures. After the Ebola pandemic, 13 percent of students in Sierra Leone and 25 percent of students in Liberia dropped out of school, with girls and low-income students most affected. 32 The socio-economic impacts of Ebola in Liberia , World Bank, April 15, 2015; The socio-economic impacts of Ebola in Sierra Leone , World Bank, June 15, 2015. Among the poorest primary-school students in Sierra Leone, dropout rates increased by more than 60 percent. 33 William C. Smith, “Consequences of school closure on access to education: Lessons from the 2013-2016 Ebola pandemic,” International Review of Education , April 2021, Volume 67. This may result in reduced employment opportunities and lifelong earnings potential for many of these students.

The potential of long-term economic damage

Education can affect not just an individual’s future earnings and well-being but also a country’s economic growth and vitality. Research suggests higher levels of education lead to increased labor productivity and enhance an economy’s capacity for innovation. Unless the pandemic’s impact on student learning can be mitigated and students can be supported to catch up on missed learning, the global economy could experience lower GDP growth over the lifetime of this generation.

We estimate by 2040, unfinished learning related to COVID-19 could translate to annual losses of $1.6 trillion to the global economy, or 0.9 percent of predicted total GDP (Exhibit 6).

Although the total dollar amount of forgone GDP is highest in the largest economies of the world (encompassing East Asia, Europe, and North America), the relative impact is highest in regions with the greatest learning delays. In Latin America and the Caribbean, pandemic-related school closures could result in losses of more than 2 percent of GDP annually by 2040 and in subsequent years.

Economic impact could be affected further if students don’t return to school and cease learning altogether.

Identifying potential solutions

The response to the learning crisis will likely vary from country to country, based upon preexisting educational performance, the depth and breadth of learning delays, and system resources and capacity to respond. That said, all school systems will likely need to plan across multiple horizons:

As 2022 began, more than 95 percent of school systems around the world were at least partially open for traditional in-person learning. 34 “Responses to Educational Disruption Survey (REDS),” UNESCO, 2022, accessed March 11, 2022. That progress is encouraging but tenuous. Many systems reopened only to close down again when another wave of COVID-19 caused additional disruptions. Even within partially open systems, not all students have access to in-person learning, and many are still attending partial days or weeks. Building resilience could mean ensuring protocols are in place for safe and supportive in-person learning, and ensuring plans are in place to provide remote options that support the whole child at the system, school, and student levels in response to future crises. School systems can also benefit by creating the flexibility to change policies and procedures as new data and circumstances arise.

COVID-19 and education: The pandemic school year has ended, but the effects of unfinished learning linger

COVID-19 and education: The lingering effects of unfinished learning

Reenrollment.

Opening buildings and embedding effective safety precautions have been challenging for many systems, but ensuring students and teachers actually turn up and reengage with learning is perhaps even more difficult. Even where in-person learning has resumed, many students have not returned or remain chronically absent. 35 Indira Dammu, Hailly T.N. Korman, and Bonnie O’Keefe, Missing in the margins 2021: Revisiting the COVID-19 attendance crisis , Bellwether Education Partners, October 21, 2021. Families may still have safety worries about in-person learning. Some students may have found jobs and now rely on that income. 36 Elias Biryabarema, “Student joy, dropout heartache as Uganda reopens schools after long COVID-19 shutdown,” Reuters, January 10, 2022. Others may have become pregnant or now act as caregivers at home. 37 Brookings Education Plus Development , “What do we know about the effects of COVID-19 on girls’ return to school?,” blog entry by Erin Ganju, Christina Kwauk, and Dana Schmidt, September 22, 2021. Still others may feel so far behind academically or so disconnected from the school environment at a social level that a return feels impossible. A multipronged approach could be helpful to understand the barriers students may face, how those could differ across student segments, and ways to support all students in continuing their educational journeys.

Systems could consider a tiered approach to support reengagement. Tier-one interventions could be rolled out for all students and include both improving school offerings for families and students and communicating about enhanced services. This might involve back-to-school awareness campaigns at the national and community levels featuring respected community members, clear communication of safety protocols, access to free food and other basic needs on campuses, and the promotion of a positive school climate with deep family engagement.

Tier-two interventions, which could be directed at students who are at heightened risk of not returning to school, may involve more targeted support. These efforts might include community events and canvassing to bring school buses or mobile libraries to historically marginalized neighborhoods, phone- or text-banking aimed at students who have not returned to school, or summer opportunities (including fun reorientation activities) to convince students to return to the school campus. At the student level, it could include providing some groups of students with deeper learning or social-emotional recovery services to help them reintegrate into school.

Tier-three interventions encompass more intensive and specialized support. These efforts may include visits to the homes of individual students or new educational environments tailored to student needs—for example, night schools for students who need to complete high school while working.

Once students are back in school, many may need support to recover from the academic and social-emotional effects of the pandemic. Indeed, while academic recovery seems daunting, supporting the mental-health and social-emotional needs of students may end up being the bigger challenge. 38 Protecting youth mental health: The U.S. surgeon general’s advisory , U.S. Department of Health and Human Services, 2021. This process starts with a recognition that each child is unique and that the pandemic has affected different students in different ways. Understanding each student’s situation, in terms of both learning and well-being, is important at the classroom level, with teachers and administrators trained to interpret cues from students and refer them to more intensive support when necessary. Assessments will likely also be needed at the school and system levels to plan the response.

With an understanding of both the depth and breadth of student needs, systems and schools could consider three levers of academic acceleration: more time, more dedicated attention, and more focused content. Implementation of these levers will likely vary by context, but the overall goals are the same: to overcome both historical gaps and new COVID-19-related losses, and to do so across academic and whole-child indicators.

In high-income countries, digital formative assessments could help determine in real time what students know, where they may have gaps, and what the next step could be for each child. More relational tactics can be incorporated alongside digital assessments, such as teachers taking the time to connect with each child around a simple reading assessment, which may rebuild relationships and connectivity while assessing student capabilities. Schools could also consider universal mental-health diagnostics and screeners, and train teachers and staff to recognize the signs of trauma in students.

Once schools have identified students who need academic support, proven, evidence-based solutions could support acceleration in high-income school systems. High-dosage tutoring, for example, could enable students to learn one to two additional years of mathematics in a single year. Delivered three to five times a week by trained college graduates during the school day on top of regular math instruction, this type of tutoring is labor and capital intensive but has a high return on investment. Acceleration academies, which provide 25 hours of targeted instruction in reading to small groups of eight to 12 students during vacations, have helped students gain three months of reading in just one week. Exposing students to grade-level content and providing them with targeted supports and scaffolds to access this content has improved course completion rates by two to four times over traditional “re-teaching” remediation approaches.

With an understanding of both the depth and breadth of student needs, systems and schools could consider three levers of academic acceleration: more time, more dedicated attention, and more focused content.

In low- and middle-income countries, where learning delays may have been much greater and where the financial and human-capital resources for education can be more limited, different implementation approaches may be required. Simple, fast, inexpensive, and low-stakes evaluations of student learning could be carried out at the classroom level using pen and paper, oral assessments, and mobile data collection, for example.

Solutions for supporting the acceleration of student learning in these contexts could start with ensuring foundational literacy and numeracy (FLN), prioritizing essential standards and content. Evidence-based teaching methods could speed up learning; for example, Pratham’s Teaching at the Right Level (TaRL) approach—which groups children by learning needs, rather than by age or grade, and dedicates time to basic skills with continual reassessment—has led to improvements of more than a year of learning in classrooms and summer camps. 39 Improvements of 0.2 to 0.7 standard deviations; assuming that one year of learning ranges from 0.2 of a standard deviation in low income countries and 0.5 of a standard deviation in high income countries, in accordance with World Bank assumptions:; João Pedro Azevedo et al., Simulating the potential impacts of COVID-19 school closures on schooling and learning outcomes , World Bank working paper 9284, June 2020; David K. Evans and Fei Yuan, Equivalent years of schooling , World Bank working paper 8752, February 2019. Even with the application of existing approaches, more time in class may be required—with options to extend the school year or school day to support students. Widespread tutoring may not be realistic in some countries, but peer-to-peer tutoring and cross-grade mentoring and coaching could supplement in-class efforts. 40 COVID-19 response–remediation: Helping students catch up on lost learning, with a focus on closing equity gaps , UNESCO, July 2020.

Reimagining

In addition to accelerating learning in the short term, systems can also use this moment to consider how to build better systems for the future. This may involve both recommitting to the core fundamentals of educational excellence and reimagining elements of instruction, teaching, and leadership for a post-COVID-19 world. 41 Jake Bryant, Emma Dorn, Stephen Hall, and Frédéric Panier, “ Reimagining a more equitable and resilient K-12 education system ,” McKinsey, September 8, 2020. A lot of ground could be covered by rolling out existing evidence-based interventions at scale—recommitting to core literacy and numeracy skills, high-quality instructional materials, job-embedded teacher coaching, and effective performance management. Recommitting to these basics, however, may not be enough. Systems can also innovate across multiple dimensions: providing whole-child supports, using technology to improve access and quality, moving toward competency-based learning, and rethinking teacher preparation and roles, school structures, and resource allocation.

For example, many systems are reemphasizing the importance of caring for the whole child. Integrating social-emotional learning for all students, providing trauma-informed training for teachers and staff, 42 “Welcome to the trauma-informed educator training series,” Mayerson Center for Safe and Healthy Children, accessed March 22, 2022. and providing counseling and more intensive support on and off campus for some students could provide supportive schooling environments beyond immediate crisis support. 43 “District student wellbeing services reflection tool,” Chiefs for Change, January 2022. A UNESCO survey suggests that 78 percent of countries offered psychosocial and emotional support to teachers as a response to the pandemic. 44 What’s next? Lessons on education recovery , June 2021. Looking forward, the State of California is launching a $3 billion multiyear transition to community schools, taking an integrated approach to students’ academic, health, and social-emotional needs in the context of the broader community in which those students live. 45 John Fensterwald, “California ready to launch $3 billion, multiyear transition to community schools,” EdSource, January 31, 2022.

The role of education technology in instruction is another much-debated element of reimagining. Proponents believe education technology holds promise to overcome human-capital challenges to improved access and quality, especially given the acceleration of digital adoption during the pandemic. Others point out that historical efforts to harness technology in education have not yielded results at scale. 46 Jake Bryant, Felipe Child, Emma Dorn, and Stephen Hall, “ New global data reveal education technology’s impact on learning ,” McKinsey, June 12, 2020.

Numerous experiments are under way in low- and middle-income countries where human capital  challenges are the greatest. Robust solar-powered tablets loaded with the evidence-based literacy and numeracy app one billion led to learning gains of more than four months 47 “Helping children achieve their full potential,” Imagine Worldwide, accessed March 22, 2022. in Malawi, with plans to roll out the program across the country’s 5,300 primary schools. 48 “Partners and projects,” onebillion.org, accessed March 22, 2022. NewGlobe’s digital teacher guides provide scripted lesson plans on devices designed for low-infrastructure environments. In Nigeria, students using these tools progressed twice as fast in numeracy and three times as fast in literacy as their peers. 49 “The EKOEXCEL effect,” NewGlobe Schools, accessed March 22, 2022. As new solutions are rolled out, it will likely be important to continually evaluate their impact compared with existing evidence-based approaches to retain what is working and discard that which is not.

Charting a potential path forward

There is no precedent for global learning delays at this scale, and the increasing automation of the workforce advances the urgency of supporting students to catch up to—and possibly exceed—prepandemic education levels to thrive in the global economy. Systems will likely need resources, knowledge, and organizational capacity to make progress across these priorities.

Even before COVID-19, UNESCO estimated that low- and middle-income countries faced a funding gap of $148 billion a year to reach universal preprimary, primary, and secondary education by 2030 as required by UN Sustainable Development Goal 4. As a result of the pandemic, that gap has widened to $180 billion to $195 billion a year. 50 Act now: Reduce the impact of COVID-19 on the cost of achieving SDG 4 , UNESCO, September 2020. Even if that funding gap were closed, the result would be increased enrollment, not improvements in learning. UNESCO estimates that just 3 percent of global stimulus funds related to COVID-19 have been directed to education , 97 percent of which is concentrated in high-income countries. 51 “Uneven global education stimulus risks widening learning disparities,” UNESCO, October 19, 2021.

In many countries, shortages of teachers and administrators are just as pressing as the lack of funding. Many teachers in Uganda weren’t paid during the pandemic and have found new careers. 52 Alon Mwesigwa, “’I’ll never go back’: Uganda’s schools at risk as teachers find new work during Covid,” Guardian , September 30, 2021. Even high-income countries are grappling with teacher shortages. In the United States, 40 percent of district leaders and principals describe their current staff shortages as “severe” or “very severe.” 53 Mark Lieberman, “How bad are school staffing shortages? What we learned by asking administrators,” EducationWeek , October 12, 2021. Fully addressing pandemic-related learning losses will require a full accounting of the cost and a long-term commitment, recognizing the critical importance of investments in education for future economic growth and stability.

Countries do not need to reinvent the wheel or go it alone. Many existing resources catalog evidence-based practices relevant to different contexts, both historical approaches and those specific to COVID-19 recovery. For high-income countries, the Education Endowment Foundation, Annenberg’s EdResearch for Recovery platform, and the Collaborative for Student Success resources for states and districts in the United States provide research-based guidance on solutions.

In many countries, shortages of teachers and administrators are just as pressing as the lack of funding.

For low- and middle-income countries, materials developed in partnership with UNESCO, UNICEF, and the World Bank include tools to support FLN, Continuous and Accelerated Learning, and teacher capacity (Teach and Coach). UNESCO’s COVID-19 Response Toolkit provides guidance across income levels. Collaboration across schools, regions, and countries could also promote knowledge sharing at a time of evolving needs and practices—from webinars to active communities of practice and shared-learning collaboratives.

Organizing for the response across these multiple levels is a challenge even for the most well-resourced and sophisticated systems. Our recent research found 80 percent of government efforts to transform performance don’t fully meet their objectives. 54 “ Delivering for citizens: How to triple the success rate of government transformations ,” McKinsey, May 31, 2018. Success will likely require a relentless focus on implementation and execution, with multiple feedback loops to achieve continuous learning and improvement.

The COVID-19 pandemic was indisputably a global health and economic crisis. Our research suggests it also caused an education crisis on a scale never seen before.

The pandemic also showed, however, that innovation and collaboration can arise out of hardship. The global education community has an opportunity to come together to respond, bringing evidence-based practices at scale to every classroom. Working together, donors and investors, school systems and districts, principals and teachers, and parents and families can ensure that the students who endured the pandemic are not a lost generation but are instead defined by their resilience.

Jacob Bryant is a partner in McKinsey’s Seattle office; Felipe Child is a partner in the Bogotá office, where Jose Espinosa is an associate partner; Emma Dorn is a senior expert in the Silicon Valley office; Stephen Hall is a partner in the Dubai office, where Dirk Schmautzer is a partner; Topsy Kola-Oyeneyin is a partner in the Lagos office; Cheryl Lim is a partner in the Singapore office; Frédéric Panier is a partner in the Brussels office; Jimmy Sarakatsannis is a senior partner in the Washington, DC, office; and Seckin Ungur is a partner in the Sydney office, where Bart Woord is an associate partner.

The authors wish to thank Annie Chen, Kunal Kamath, An Lanh Le, Sadie Pate, and Ellen Viruleg for their contributions to this article.

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Nearly 147 million children missed more than half of their in-person schooling between 2020 and 2022. Image:  Unsplash/Taylor Flowe

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  • As well as its health impacts, COVID-19 had a huge effect on the education of children – but the full scale is only just starting to emerge.
  • As pandemic lockdowns continue to shut schools, it’s clear the most vulnerable have suffered the most.
  • Recovering the months of lost education must be a priority for all nations.

When the World Health Organization declared COVID-19 to be a pandemic on 11 March 2020, few could have foreseen the catastrophic effects the virus would have on the education of the world’s children.

During the first 12 months of the pandemic, lockdowns led to 1.5 billion students in 188 countries being unable to attend school in person, causing lasting effects on the education of an entire generation .

As an OECD report into the effects of school closures in 2021 put it: “Few groups are less vulnerable to the coronavirus than school children, but few groups have been more affected by the policy responses to contain the virus.”

Although many school closures were announced as temporary measures, these shutdowns persisted throughout 2020 – and even beyond in some cases.

As late as March 2022, UNICEF reported that 23 countries, home to around 405 million schoolchildren, had not yet fully reopened their schools . As China battled to contain new COVID-19 outbreaks, schools were closed in Shanghai and Xian in October 2022.

COVID has ended education for some

Nearly 147 million children missed more than half of their in-person schooling between 2020 and 2022, UNICEF says. And it warns that many, especially the most vulnerable, are at risk of dropping out of education altogether.

The danger is highlighted by UNICEF data showing that 43% of students did not return when schools in Liberia reopened in December 2020. The number of out-of-school children in South Africa tripled from 250,000 to 750,000 between March 2020 and July 2021, UNICEF adds.

When schools in Uganda reopened after being closed for two years, almost one in ten children were missing from classrooms. And in Malawi, the dropout rate among girls in secondary education increased by 48% between 2020 and 2021.

A graphic showing the deepening learning crisis.

Out-of-school children are among the most vulnerable and marginalized children in society, says UNICEF. They are the least likely to be able to read, write or do basic maths, and when not in school they are at risk of exploitation and a lifetime of poverty and deprivation, it says.

Lost learning time

Even when children are in school, the amount of learning time they have lost to the pandemic is compounding what UNICEF describes as “a desperately poor level of learning” in 32 low-income countries it has studied.

“In the countries analyzed, the current pace of learning is so slow that it would take seven years for most schoolchildren to learn foundational reading skills that should have been grasped in two years, and 11 years to learn foundational numeracy skills,” the charity says.

A graphic showing estimated impacts of COVID-19 on learning poverty.

Analysis of the crisis by UNESCO, published in November 2022, found that the most vulnerable learners have been hardest hit by the lack of schooling. It added that progress towards the United Nations Sustainable Development Goal for Education had been set back.

In Latin America and the Caribbean – a region that suffered one of the longest periods of school closures – average primary education scores in reading and maths could have slipped back to a level last seen 10 years ago , the World Bank says.

Four out of five sixth graders may not be able to adequately understand and interpret a text of moderate length, the bank says. As a result, these students are likely to earn 12% less over their lifetime than if their education had not been curtailed by the pandemic, it estimates.

Widening the achievement gap

In India, the pandemic has widened the gaps in learning outcomes among schoolchildren with those from disenfranchised and vulnerable families falling furthest behind, according to a 2022 report by the World Economic Forum.

Even where schools tried to keep teaching using remote learning, the socio-economic divide was perpetuated. In the United States, a study found children’s achievement in maths fell by 50% more in less well-off areas , compared to those in more affluent neighbourhoods.

One year on: we look back at how the Forum’s networks have navigated the global response to COVID-19.

Using a multistakeholder approach, the Forum and its partners through its COVID Action Platform have provided countless solutions to navigate the COVID-19 pandemic worldwide, protecting lives and livelihoods.

Throughout 2020, along with launching its COVID Action Platform , the Forum and its Partners launched more than 40 initiatives in response to the pandemic.

The work continues. As one example, the COVID Response Alliance for Social Entrepreneurs is supporting 90,000 social entrepreneurs, with an impact on 1.4 billion people, working to serve the needs of excluded, marginalized and vulnerable groups in more than 190 countries.

Read more about the COVID-19 Tools Accelerator, our support of GAVI, the Vaccine Alliance, the Coalition for Epidemics Preparedness and Innovations (CEPI), and the COVAX initiative and innovative approaches to solve the pandemic, like our Common Trust Network – aiming to help roll out a “digital passport” in our Impact Story .

Consultancy firm McKinsey says that US students were on average five months behind in mathematics and four months behind in reading by the end of the 2020-21 school year. Disadvantaged students were hit hardest, with Black students losing six months of learning on average.

A graphic showing that by the end of 2020-21 school year, students were on average five months behind in math and four months behind in reading.

Researchers in Japan found a similar pattern, with disadvantaged children and the youngest suffering most from school closures. They said the adverse effects of being forced to study at home lasted longest for those with poorest living conditions .

However, in Sweden, where schools stayed open during the pandemic, there was no decline in reading comprehension scores among children from all socio-economic groups, leading researchers to conclude that the shock of the pandemic alone did not affect students’ performance.

Getting learning back on track

So what can be done to help the pandemic generation to recover their lost learning ?

The World Bank outlines 10 actions countries can take, including getting schools to assess students’ learning loss and monitor their progress once they are back at school.

A graphic showing opportunities to make education more inclusive, effective and resilient that it was before the crisis.

Catch-up education and measures to ensure that children don’t drop out of school will be essential, it says. These could include changing the school calendar, and amending the curriculum to focus on foundational skills.

There’s also a need to enhance learning opportunities at home, such as by distributing books and digital devices if possible. Supporting parents in this role is also critical, the bank says.

Teachers will also need extra help to avoid burnout, the bank notes. It highlights a “need to invest aggressively in teachers’ professional development and use technology to enhance their work”.

Have you read?

Covid-19 has hit children hard. here's how schools can help, how the education sector should respond to covid-19, according to these leading experts, covid created an education crisis that has pushed millions of children into ‘learning poverty’ -report, don't miss any update on this topic.

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  • Open access
  • Published: 27 February 2024

Evaluating a peer-to-peer health education program in Australian public housing communities during the COVID-19 pandemic

  • Jane Oliver 1 , 2 ,
  • Angeline Ferdinand 3 ,
  • Awil Hussein 4 ,
  • Ruqiyo Hussein 1 ,
  • Jessica Kaufman 2 ,
  • Peta Edler 1 ,
  • Nicole Allard 1 , 5 ,
  • Margie Danchin 2 , 6 , 7 &
  • Katherine B. Gibney 1  

BMC Health Services Research volume  24 , Article number:  250 ( 2024 ) Cite this article

146 Accesses

Metrics details

The cohealth Health Concierge program operated in Melbourne, Australia from July 2020 to 30 June 2022. It provided peer-to-peer support to culturally and linguistically diverse residents of high-rise public housing. During this time, the COVID-19 public health response changed frequently and included movement restriction, testing and vaccination. We conducted a mixed-methods evaluation to determine the Health Concierge program’s impact on residents’ engagement with health services and public health activities.

The evaluation, informed by a Project Reference Group, used the RE-AIM framework. We analysed data from 20,901 routinely collected forms describing interactions between Concierges and residents from August 2021 to May 2022. Additional evaluation-specific data were collected between March and May 2022 in four housing estates; we surveyed 301 residents and conducted 32 interviews with residents, Concierges and program stakeholders.

Concierges promoted COVID-safe behaviours; linked residents with support, testing and vaccination services; and disseminated up-to-date information. Of the 20,901 recorded interactions, 8,872 (42%) included Concierges providing support around COVID-19 vaccination. Most surveyed residents (191/301, 63%) reported speaking with a Concierge in the previous six months. The self-reported two-dose COVID-19 vaccine uptake was 94% (283/301). Some residents described having meaningful, appreciated conversations with Concierges, and some described superficial interactions. While residents initially welcomed the program, many felt it failed to evolve. Poorly defined management and hiring criteria led to variable program implementation. A need for bicultural workers to continue linking residents with services was discussed.

Conclusions

Concierges’ impact on residents may have contributed to high community uptake of COVID-19 testing and vaccination, and had benefits beyond the COVID-19 remit. We recommend the program be revised and continued to inform further preparedness planning and support service access generally. Program models such as this have potential to inform and reassure high-risk communities during a pandemic. In addition, such programs can help overcome vaccine hesitancy and promote protective health behaviours, regardless of whether a pandemic is currently occurring. Ensuring these programs remain responsive to the changing needs of end-users needs over time is imperative.

Peer Review reports

Introduction

Community health workers (CHWs) have been employed in many settings to link communities with health systems and perform health promotion. While programs that engage CHWs to disseminate health information vary, evidence suggests that peer-based education may facilitate behavioural changes [ 1 ] and CHWs work can increase adherence to public health advice and medical treatment. Drawing on CHWs’ knowledge also may help clinical colleagues’ to improve their understanding of their patients [ 2 ]. The localized and place-based nature of many programs which involve CHWs disseminating health information is often critical for improving health outcomes [ 3 , 4 ].

CHWs have been reported to be active in public housing communities in Victoria, Australia [ 5 , 6 ]. During the 1960s, the Housing Commission of Victoria organised construction of high-rise residential estates for public housing tenants across 32 sites in 19 suburbs of Melbourne [ 7 ]. Communities at these sites are culturally and linguistically diverse (CALD), with many residents (or their parents) born in low/middle-income countries and moving to Australia as refugees [ 8 ]. Residents of these estates were disproportionately impacted by the COVID-19 pandemic [ 9 ]. Prior to July 2020, widespread transmission of SARS-CoV-2 had been avoided in Melbourne. In early July 2020, detection of 23 COVID-19 cases in Melbourne public housing estates prompted fears of rapid transmission [ 10 ]. In response, on 4 July 2020 ‘Operation Benessere‘ commenced [ 9 , 11 ], with approximately 3,000 residents of estates in two suburbs - Flemington and North Melbourne– placed in ‘hard lockdown’. With no advance warning, these residents were detained in their apartments by order of the Victorian Government for a period of 5–14 days. This restriction was in addition to COVID-19-related public health restrictions applied throughout Melbourne, including among the world’s longest cumulative periods spent in lockdown during 2020–2021 [ 9 ]. In December 2020 the Victorian Ombudsman concluded Operation Benessere breached residents’ human rights [ 9 ]. In September 2020, following Operation Benessere, the Victorian Government initiated the High Risk Accommodation Response (HRAR) in public housing and other high-risk accommodation settings [ 12 , 13 ]. HRAR was a partnership between 24 lead community healthcare provider organisations and the Victorian Government [ 14 ]. Outreach services were provided to residents, many of whom had unmet health needs and little prior contact with support services [ 15 ]. HRAR’s core functions included catchment planning, community engagement, prevention and preparedness activities, and supporting COVID-19 outbreak responses. Over time, HRAR evolved to include supporting residents’ access to COVID-19 testing and vaccination services [ 16 ]. cohealth, one of Victoria’s largest community providers [ 17 ], provided health and wellbeing services to residents undergoing Operation Benessere [ 9 ] and later provided additional services to residents in public housing as a HRAR lead provider [ 6 ]. Key dates relevant to this evaluation period are listed in Supplementary Fig.  1 .

The cohealth Health Concierge program (‘the program’) was designed to provide place-based peer-to-peer COVID-19 education to residents of high-rise estates as part of HRAR [ 5 , 6 ]. Concierges were stationed in foyers of 31 residential high-rises in eight Melbourne suburbs. Their role was to share information about public health restrictions, COVID-19 and health services (including testing and vaccination), and act as community supports. A major focus was providing accurate, up-to-date, understandable COVID-19 information following a morning briefing with the onsite cohealth nurse manager [ 5 , 6 ]. cohealth reportedly employed more than 150 Concierges, the majority from bicultural and multilingual backgrounds. Many Concierges were public housing residents– some dwelled in the estate where they worked [ 18 ]. cohealth’s Concierge training included short courses in COVID-19 safety, de-escalation and emotional intelligence. In addition, daily de-briefing opportunities with the on-site nurse and Client Services Officer were offered [ 18 ]. Concierges worked alongside cohealth health and support staff [ 19 ]. Service hours varied; at most with Concierge stations staffed 7-days per week from 9am until 6pm [ 20 ]. Concierges also helped organise community wellbeing activities [ 21 ]. Prior to our evaluation, media reports indicated the program was promoting COVID-19-safe behaviours including COVID-19 vaccine uptake [ 22 , 23 ]. Concierges themselves were said to have benefited from their work [ 5 , 19 ]. In March 2022, the Victorian Healthcare Association (the peak body representing the public health sector and supporting Victoria’s public health services) proposed embedding the HRAR model of care within the Victorian public health system [ 24 ]. However, pandemic response funding and the Concierge Program were discontinued from 30 June 2022, prior to transitioning to a new program (called ‘Community Connectors’) and a ‘COVID-normal way of life.’ [ 15 ].

To assess the impact of the program on high-rise public housing residents’ engagement with health services and public health activities, we conducted a mixed-methods evaluation. This evaluation was planned and initiated prior to the discontinuation of the program.

We used a co-design evaluation methodology based around the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework, and the consolidated criteria for reporting qualitative research (COREQ) framework for reporting [ 25 , 26 ]. A Project Reference Group (PRG), including residents, Concierges, other cohealth staff and Victorian Government Department stakeholders guided all study processes including the study design, results interpretation and recommendations made. Each PRG session was structured to prioritise residents’ opinions being heard.

This evaluation was undertaken in from March to May 2022 in four distinct high-rise public housing estates with large populations and where the program was well established: North Melbourne and Flemington (exposed to Operation Benessere), and Carlton and Collingwood (selected due to having comparable population sizes but were not directly impacted by Operation Benessere). These four estates were selected to investigate whether residents’ trust scores differed according to whether their estate was exposed to Operation Benessere. Supplementary Fig.  2 displays locations of the included estates.

In March 2022, each included estate housed between 1,295 and 1,965 residents. More than half the residents in each estate were reported to have CALD backgrounds, with Somalia, Ethiopia, Vietnam and China the most common countries of birth after Australia (Source: Private communication from the Victorian Government Department of Families, Fairness and Housing (DFFH) addressed to Jane Oliver; 1 July 2022) .

RE-AIM indicators and data sources

Health concierge interaction forms.

cohealth required Concierges to record each interaction with residents using an Interaction Form (Appendix 1 ). These routinely collected data contributed to the reach and effectiveness aspects of the evaluation, and included the date of the interaction, the location, and the type of supports the Concierge provided. Residents provided scores from 1 to 10 (10 being highest) for how useful they found the Concierge’s service, and how likely they were to recommend the service to friends or family. These scores were reported to the Concierge, who recorded it. cohealth provided Interaction Form data from included estates for analysis, covering 1 August 2021 to 12 May 2022 (9.5 months).

A survey co-designed with the PRG comprised 35 short-answer/multiple choice questions (Appendix 2 ) contributing data on program reach, effectiveness and adoption. It assessed Concierge competence, Concierge respectful communication and trust in public health authorities. Questions drew on two standard questionnaires [ 27 , 28 ] adapted to refer to Concierges and an infectious disease outbreak. Participants were asked to self-report the number of COVID-19 vaccines and COVID-19 tests they had ever had, and their trust in COVID-19 vaccines (assessed using Likert scales). We aimed to survey approximately 75 residents aged > 15 years in each of the four included estates. This sample size was selected to provide 80% power to detect an exposure difference of 11% between estates that had, and had not, been exposed to Operation Benessere.

Five Research Assistants administered the survey to residents using iPads. They were trained in data collection the week prior and had never worked as Concierges or cohealth employees. Three were residents of Flemington and North Melbourne estates, however they did not collect data within their estate. The other two did not currently live in public housing, however they were familiar with the study sites through lived experience. Between them, the Research Assistants were fluent in English, Somali, Arabic, Malay and Vietnamese. Convenience sampling in shared community areas occurred during weekdays (at Carlton, Collingwood and North Melbourne) and weekends (in Collingwood and Flemington). Research Assistants approached potential participants using a language they thought the person might understand. If interest was indicated, they explained the evaluation and survey questions to residents using English/other languages, as appropriate. Sometimes residents chose to act as interpreters enabling other residents to compete the survey when they could not otherwise communicate with the research team. Surveying was performed from 16 March to 2 April 2022. Participants were thanked with a $20 gift voucher.

Semi-structed interview guides were developed by qualitative researchers within the Evaluation Team and refined with advice from the PRG (Table  1 , Appendices 3 – 6 ). Interviews contributed data on all indicators of the RE-AIM framework. Residents, Concierges, other cohealth staff and other stakeholders were interviewed (Table  1 ). ‘Other stakeholders’ worked with residents or Concierges but were not cohealth employees or residents, and included staff from Foundation House, a not-for-profit residential rehabilitation centre which provided support services to Concierges.

Participants spoke until they indicated they had nothing to add. Interviews could be conducted in any language with the interviewer translating and explaining as necessary with audio recording enabled. No interview participants were known to the interviewers. No others were present during the interviews. Participants were interviewed once.

Interaction Form data underwent simple descriptive analyses.

For survey data, median scores for Concierge Competence, Concierge Respectful Communication, and Trust in Public Health Authorities were calculated by converting all responses in each survey category to a score between 0 and 3, then taking the mean value for each category. Questions were weighted equally. The Mann Whitney U test was used to compare median scores between estates. Multivariate ordinal regression estimated the adjusted odds ratio (aOR) with 95% confidence intervals (CIs) of an increased category score, in order to identify any predictor variables. Possible predictor variables considered were: participant demographics; number of COVID-19 tests; number of COVID-19 vaccines; and whether the estate had experienced hard lockdown. This enabled subgroups with higher and lower scores to be identified. Results were considered statistically significant where p  < 0.05. The proportional odds assumptions were tested by undertaking a series of binary logistic regression models (with the outcome dichotomised e.g. by comparing scores of < 5 with scores of 5 or more) and ensuring the coefficients for each covariate were similar.

Interview audio recordings were de-identified and transcribed. The interviewer transcribed the Somali interviews and translated the transcript to English. JO performed an initial inductive descriptive thematic analysis. After reviewing the transcripts, JO categorised data into codes and sub-codes, then grouped codes thematically using NVivo version 12 plus [ 29 ]. JO used a virtual whiteboard (miro.com) to identify common and unique themes and sub-themes, then refined and named themes with input from the study team and the PRG. The PRG provided direction around contextualising the themes and making recommendations that would help to meet the residents’ needs. Final themes were organised deductively within the RE-AIM framework [ 25 ]. Quotes are provided to support the thematic analysis, some modified slightly to assist readability.

We obtained 20,901 Interaction Forms (routinely collected August 2021–May 2022), surveyed 301 residents (March–April 2022), and interviewed 32 stakeholders (April–May 2022).

The majority of surveyed participants were female ( n  = 212; 70.4%). Many ( n  = 249; 82.7%) mostly spoke languages other than English at home, most often Somali (37.9%; n  = 114). Eleven participants (3.7%) were Aboriginal and/or Torres Strait Islanders. The median age was 45 years (range 16–101 years; Table  2 ).

Interviewed stakeholders comprised 14 residents (2–5 from each estate); eight Concierges (1–3 from each estate), five other cohealth staff; three DFFH staff and two Foundation House staff. Interviews with residents tended to be briefer than with other participants (mean duration 17 min versus 41 min). Thirteen of 14 interviewed residents mostly spoke a language other than English at home: seven spoke Somali, three Arabic, and one each of Cantonese, Hindi and Tigrigna. As 37 residents were invited, the resident interview response rate was 38%, with 23 not responding (none explicitly declined). No participants chose to review their interview transcripts. Two face-to-face interviews were conducted in Somali by Research Assistants who resided in included estates (RH; female, Bachelor of Applied Science, no prior qualitative research experience. SA; female, no tertiary qualifications or prior qualitative research experience). They had received two training sessions with JO (Research Fellow). Due to their limited availability, JO (female, PhD in public health, qualitative research experience) conducted all other interviews in English; six were face-to-face; 11 residents were interviewed by phone and 10 cohealth staff / other stakeholder interviews were via Zoom.

Key themes are presented according to the dimensions of the RE-AIM framework and related study outcome (Table  3 ).

Reach: everyone knows they’re there

Three-quarters of surveyed residents (225/301, 75%) reported ever having received information about testing from a Concierge, 92.0% of whom (207/225) reported they thought this information was accurate. Most surveyed residents (88%; 266/301) reported they had been tested for COVID-19 at least once, and 30% (91/301) reported being tested more than five times. A similar proportion (73%; 219/301) reported ever having received information about COVID-19 vaccines from a Concierge, 93% (203/219) of whom thought this information was accurate. Two-thirds, (191/301, 63%) reported speaking to a Concierge in their building in the prior six months; 14% (42/301) spoke to a Concierge more than five times in that period.

When interviewed, residents immediately recalled seeing Concierges and understood their role was to provide COVID-19 information and facemasks. Some remarked on seeing Concierges often and when asked, most said they would feel comfortable talking with a Concierge.

“I’ve seen [Concierges] a lot. I would just go up to them, especially because they are under my building, like “hey is it okay if I have face masks?” Resident #7, Carlton.

Interaction Form data showed Concierges provided COVID-19 related support in all 20,901 recorded interactions. In addition, ‘other’ support was provided in 78.3% ( N  = 16,365) of interactions, most commonly ‘Other health-related information’ (Table  4 ). On average, 5.5 supports were provided per interaction (range: 1–8 supports/interaction). Language support was provided in 8.7% ( N  = 2,359) of interactions.

Nearly all (96.0%; n  = 289/301) surveyed residents reported having received at least one COVID-19 vaccine, with 94.0% ( n  = 283) reported they had completed the recommended two-dose primary vaccine course. Nearly half ( n  = 39, 46.2%) reported they had received a booster (third) dose, lower than the proportion of eligible Victorians who had received a booster (65.9% as of 17 March 2022; all participants were eligible) [ 31 ]. Trust in COVID-19 vaccines was fairly high; 75.7% ( n  = 228) stated they moderately/very much trusted COVID-19 vaccines (Fig.  1 ).

figure 1

Likert scores assigned by survey participants– ( A ) Concierge healthcare competence, ( B ) Concierge respectful communication, ( C ) Trust in Victorian public health authorities, ( D ) Trust in COVID-19 vaccines

Effectiveness: concierges were great at the start of the pandemic, they don’t do so much now

The median score from the Interaction Form data regarding satisfaction with the Concierge Service was 10 out of 10 (range: 1–10; IQR: 10–10), although these scores may have been affected by response bias. Residents shared experiences of Concierges supporting them whilst in home isolation, bringing supplies and keeping them updated on COVID-19-related news. Interviewed residents often identified Concierges as an important source of up-to-date information when public health advice and restrictions were rapidly changing. A few residents described Concierges teaching them ways to protect themselves from COVID-19 and being reminded to remain vigilant upon seeing them. One resident shared discussing COVID-19 vaccination with a Concierge.

‘Because of the information they give me, that lead me to take the vaccination. To protect myself and my children.… I’m immunocompromised.’ Resident #10, Collingwood .

Generally, however, residents expressed there was no ongoing need for Concierges to be stationed in foyers sharing COVID-19 information once the public health response moved away from tight suppression approaches.

When the pandemic start, that’s when I feel like they [Concierges] were more useful… now, they just sit there. Resident #1, North Melbourne.

Some Concierges disagreed, saying that residents came to them for reassurance as COVID-19 case numbers increased. While Concierges described working hard to support residents, some said they felt restricted behind their desks and wanted to engage more with the community.

Superficial interactions with Concierges while obtaining face masks and hand sanitizer were commonly described by residents, especially as pandemic restrictions eased. Residents often described Concierges as a source of simple, practical COVID-19 advice, such as where to get a COVID-19 vaccine. Residents largely perceived Concierges as having limited education and training, and therefore not being able to answer technical questions, which they would save for a medical provider. Most residents said they would be unwilling to discuss non-COVID-19 health matters with Concierges.

“I didn’t ask them [Concierges] much questions, I just wanted to know where I could get the vaccination and they told me… I feel like they are not like educated… the doctor would know more.” Resident #1, North Melbourne .

The median Concierge Health Care Competence Score was 2 out of 3 (IQR:1–3), equivalent to surveyed residents reporting the service was provided competently most of the time. The multivariate ordinal regression model indicated that residents who had > 5 COVID-19 tests had higher odds of giving a higher Competence Score compared to residents who had ≤ 5 COVID-19 tests (aOR: 2.47; 95% CI: 1.35–4.51; p -value: 0.003; Supplementary Table 1 ).

The median Respectful Communication Score was 3 out of 3 (IQR:1–3), equivalent to residents reporting Concierges communicated respectfully all the time. The proportion of participants assigning different score categories is shown in Fig.  1 . No predictors of a higher Respectful Communication Score were identified by the model.

A subtheme concerned under-recognition of the full impact of Concierges’ work. cohealth staff discussed Concierges’ organising community services and events, including clothing libraries, walking groups and festivals. Some suggested the residents simply saw these as being cohealth-run and were unaware of the Concierges’ contributions.

cohealth staff discussed Concierges’ insights as critical to informing their health service; particularly mass COVID-19 vaccination and testing services.

“[Concierges]….were saying, “can you change your operating hours to 12 to 6:30, so you can capture everyone coming home?” “Can we make sure we have got these languages on this day?” cohealth staff #1 .

Several cohealth staff felt that the program was constrained by its COVID-19 funding remit, and the impact of Concierges work was not captured by HRAR indicators, nor recognised by funders.

“…we [cohealth] will continue to step into some other issues that are onsite like mental health.…[the program] it’s been labelled COVID response.…it’s more than that.” cohealth staff #5 .

Adoption: inconsistent program adoption within communities

No interviewed residents said that a Concierge would be their first source of COVID-19 advice; most would initially look at Health Department webpages. Despite this, some, including those who had very limited interactions with Concierges, said they felt the program helped residents and it was a shame it would not continue. cohealth staff and Concierges emphasised the close, trusted relationships which they perceived Concierges had built with residents, and spoke of how this trust carried through to other cohealth services. Conversely several residents and Concierges discussed residents being too busy to speak with Concierges, and some residents described feeling people in their high-rises had very little connection to Concierges.

‘They [Concierges] are just sitting at the bottom at the door and people are going and coming… after some time, people just started ignoring them.…They didn’t even connect with the community around here.’ Resident #2, North Melbourne .

Some residents strongly criticized Concierges for their perceived lack of engagement. They said the estate resident-leaders had to step in to support vulnerable neighbours through the pandemic and promote COVID-19 vaccine uptake themselves as no-one else was.

“The Concierge is… there with the paper [COVID-19 vaccine pamphlet], but the African communities [residents], they are not believing the paper. Some people can’t read… But as a community leader,… we mention how easy and important [COVID-19 vaccination] is… The community leaders, they are working hard to get the community to do it, but I don’t know about the Concierge.” Resident #11, Flemington .

Meanwhile, other residents, Concierges and other cohealth staff described Concierges providing highly varied, impactful support to residents, not limited to COVID-19/related issues.

“[Concierges] They’re friendly people. And they’re working very hard. They will give you information. They are so nice…. They understand what the community needs. They are part of the community too.” Resident #3, Collingwood .

Surveyed residents scored their trust in the Victorian public health authorities’ ability to respond to a localised infectious disease outbreak highly. The median confidence score was 3 (‘very confident’); range 0–3; IQR: 2–3. No difference in the median score was observed according to whether or not estates experienced Operation Benessere ( p  = 0.52). Participants who mostly spoke languages other than English at home scored higher (aOR: 2.74 [95% CI: 1.49–5.07]; p -value: 0.001), as did participants who reported having ≥ 2 COVID-19 vaccine doses compared to participants who had < 2 COVID-19 vaccine doses (aOR: 5.27 [95%CI: 1.89–14.7]; p -value: 0.0002; Supplementary Table 1 ).

Implementation: unclear expectations led to variable service

Poorly defined hiring criteria were sometimes said by interviewed participants to have resulted in inappropriate people becoming Concierges. The merits of employing residents were often discussed. A need for this to occur in a meaningful way, employing people who actually engaged with, and represented, the community they served came up frequently. Concierges sometimes discussed a lack of clarity around their management and reporting pathways, inadequate supervision and unclear expectations around what they were required to do. This confusion was said to have caused some Concierges to disengage, feel bored and behave unprofessionally.

“…we had management around sometimes, but generally we were just left to our own devices. That was pretty ugly. And it meant also that the Concierges who were always naughty got even naughtier.” Concierge #7 .

Some Concierges felt the training they had received was too little, too late. This view was also articulated by a Foundation House employee.

‘I think by 2021, [ Concierges ] were more confident in the role but… there didn’t really seem to be much change in the support available for them, especially around risky situations [aggression from residents].…but people still kept loving the job.’ Other stakeholder #5 .

Maintenance: there’s so much more we could do

cohealth staff and Concierges commonly voiced a perception that not expanding the program was a missed opportunity. Interviewed people, including residents, discussed a need for a community health hub, or similar service, where bicultural workers could provide residents with referrals to varied service providers, conduct health promotion and act as an information point. Residents spoke of their estates having building maintenance issues, residents needing language support, experiencing food insecurity, and requiring greater access to medical, oral and mental health services. Some discussed how Concierges helped to address these issues.

…he [a Concierge] thought it would be really important to create a space, initially where men could talk about mental health, so he created a wellness walk, which we do on most Tuesday and Thursday mornings and now it’s sort of open to everybody. cohealth staff #3

Interviewed concierges spoke of the sense of purpose their work brought them and how much they enjoyed helping residents. This came despite more difficult aspects of the role, particularly having to work in uncomfortable temperatures and sometimes facing aggression. Several single mothers described how working flexible hours on-site allowed them to have employment despite their caring responsibilities. Concierges expressed grief, anger and worry that their roles were to soon end abruptly. They described feeling unappreciated and used by government, and also by cohealth. They discussed their perceptions that their work benefitted end-users and there was a need for it to continue. Concierges shared sacrifices they had made for their work, the emotional toll of working at the estate they lived in, and their dedication to helping others.

[Said through tears] ‘Upset, worried. Very upset with how it is and we help the community a lot. Why is the job gone? And we were working very hard for this government.’ Concierge #6 .

Key findings

The program had an extensive reach, with 20,901 interactions recorded over 9.5-months and nearly two-thirds of surveyed residents having spoken with a Concierge in the last six months. Through being stationed in residential high-rises with on-site health workers and support staff, Concierges could rapidly update residents, provide reassurance and support outbreak investigations. This was despite strict public health restrictions frequently limiting in-person interactions [ 32 ]. Most surveyed residents (73–75%) reported obtaining information from a Concierge about COVID-19 vaccines and testing, and Interaction Forms indicated the Concierges provided very frequent support to get tested and vaccinated (53% and 45% of interactions, respectively). Our surveys indicate the Concierge service was mostly delivered competently and Concierges communicated respectfully. There was a perception that the program became less effective, with the scope needing to be modified repeatedly to meet residents’ needs as pandemic restrictions eased. Some participants criticised Concierges over a perceived lack of engagement, especially once most pandemic restrictions had lifted. Others said Concierges helped the residents. There was some tension between residents seeing Concierges as approachable peers, yet perceiving them as unable to answer technical questions, which they would save for medical professionals. Variable implementation was apparent, with management described as inconsistent across sites, and Concierge personnel not necessarily representing the residents they served. A need to maintain a revised program that sought to address outcomes beyond COVID-19 was often discussed in the context of high-needs communities. Participants commonly suggested that a revised program employing bicultural workers to conduct health promotion and continue to link residents with support services would be beneficial.

Recommendations

Considering our study data and advice from the PRG, we made four recommendations for a continued Program (Fig.  2 ).

figure 2

Recommendations for an extended cohealth Health Concierge Program in high-rise public housing estates

Revise and continue the program long-term

There is a clear need for two-way communication and engagement in Melbourne’s multicultural public housing estates. The program brought together residents, Government and services with bicultural staff. A strength was the program’s ability to facilitate residents’ access to qualified health professionals, and disseminate information in multiple languages. This engagement should continue as an ongoing commitment to supporting residents, with a revised program seeking to improve residents’ access to health information and a variety of support services. Mechanisms incorporate feedback from residents should be in-built to enable program delivery and scope to be refined to address changing community needs.

Expand beyond COVID-19

Ultimately Concierges have a dual health promotion and service navigation role. The full scope of the role should be co-designed by residents. To justify the investment, any program extension should include planned, co-designed periodic evaluation with clear outcome measures. The impact of the role on Concierges themselves should be considered.

Integration

Building mutually supportive relationships between Concierges and community leaders supports residents’ service uptake. The program should work closely with local public health units (LPHUs) to ensure residents’ needs are understood and met in a timely manner, with any funding opportunities available through LPHUs optimised.

Clear roles, management and training

Flexible work and professional development opportunities would enable Concierge hiring to be inclusive and represent the community. Clear guidelines around applicant selection should be available. With a deep understanding of the people they serve, Concierges can advise stakeholders on community matters. Concierge selection criteria needs to include people who can support older adults and residents with complex health needs. Well-defined roles, management and training will promote staff accountability. This would be assisted by dedicated, site-specific managers who understand the range of services available to residents. We recommend Concierges be trained to access reliable health information such as Health Translations and Better Health Channel . Additional training could include first aid and English proficiency. Formal opportunities to de-brief with management should continue on the daily basis cohealth employed, as well as informal debriefing between Concierges.

Many of our (independent) recommendations are similar to those made by researchers following a survey of 865 HRAR residents [ 13 ]. These authors recommended making the health sector easier for residents to access, using assertive health outreach strategies, providing wrap-around services, and supporting service access [ 13 ]. A qualitative study wherein 19 (mostly older adult) residents of Flemington and North Melbourne estates were interviewed also recommended residents be consulted on public health policies which affect them, similar to our evaluation. That study concluded supports provided through Concierges were highly valued by residents and should be ongoing [ 33 ]. Our interview findings echo findings of a cohealth survey of 1,181 high-rise public housing residents across five Melbourne city councils in the first quarter of 2022; including estates in this evaluation. This identified residents’ top five priorities as: social activities to reconnect with the community; addressing mental health concerns; addressing other health concerns; gaining employment and financial security; and improving their health and fitness [ 34 ]. It is possible Concierges helped some residents address these priorities through providing information about services and organising activities.

Public health context

In Australia, as of June 2022, people born overseas were two-times more likely die from COVID-19, in part, is due to lower COVID-19 vaccine uptake [ 35 ]. The need to increase CALD groups’ trust in COVID-19 vaccines was apparent from the start of the vaccine roll out in early 2021 [ 36 , 37 ].

Similar to the Concierge Program, CHWs have been widely engaged in the COVID-19 pandemic response in low-middle income countries and in vulnerable communities in high-income countries [ 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 ]. During the pandemic, CHWs who were trusted community members played a key role as peer-educators, including in Indigenous Australian communities [ 44 , 47 , 56 ]. A international review of CHWs in pandemics concludes that, if adequately resourced, CHWs are critical in mitigating harm, and can help maintain essential services [ 57 ]. There are limited empirical data on CHWs’ impact on community engagement with health services and public health activities, although a Ugandan study showed participants valued CHWs’ COVID-19 home talks more than information on the radio [ 42 ]. This illustrates the value of two-way peer-led conversations in the pandemic response.

This program model may be adapted to disseminate health information in other geographically-concentrated priority groups. Groups whose trust in public authorities has been eroded are particularly at-risk from misinformation [ 58 ]. Public trust was a key factor in the ultimate success of the Victorian pandemic response– not only in the official public health messaging, but also in translations of those messages [ 59 ]. Peer-led information dissemination programs therefore have potential to overcome mistrust of authorities. Potentially a more timely outbreak response using a co-designed community participation model, such as this program, could have addressed increasing local COVID-19 transmission and averted the hard lockdown of July 2020.

In July 2022, $8.5 million in funding for a new community health program was announced by the Victorian Government. This program is called ‘Community Connectors’ and is funded for 12 months (to June 2023) in high-rise public housing estates through Homes Victoria. Community Connectors’ goal is to improve health and well-being by employing local residents to deliver targeted health promotion and connection services [ 60 ]. ( Source: Private communication from cohealth addressed to Jane Oliver 28 July 2022) . Through our engagement with cohealth and government stakeholders, this evaluation helped shape the Community Connectors program.

Strengths and limitations

A strength of this evaluation is its co-design aspect, ensuring our recommendations are relevant and acceptable to end-users. Further strengths include the breadth of views held by interviewed stakeholders, and the quantity and detail of the quantitative data.

We were unable to establish a direct quantifiable association between the Concierges’ work and outcomes. Concierges may have had important roles increasing COVID-19 vaccine confidence, however the drivers of vaccine uptake are complex and multifactorial [ 61 ]. Other impacts, such as some Concierges receiving their first employment in Australia, were not captured. Although Concierges were involved in multiple outbreak responses in 2021, quantitative data on their contributions were not available. Despite this, indirect evidence of impact was apparent in all the data sources we analysed. We did not receive Interaction Form data for the initial 10-months of program implementation, so are unable to examine temporal changes in the types of supports provided and residents’ engagement with Concierges. Furthermore, we note that some Interaction Form fields lacked detail, and residents reporting their satisfaction scores to Concierges may well have introduced positive response bias. As we used convenience sampling, participants may not be representative of the entire resident population. The low (38%) resident interview response rate may also have introduced bias. While thematic saturation was not noted when analysing resident interviews, this was noted among all other stakeholder groups. This, and the relatively small sample of 14 residents (most of whom were interviewed in English by a non-community member) is a limitation. Due to resource constraints, a single researcher (JO) performed the interview data analysis. Reliance on a single person’s interpretation may have affected our findings, however guidance was provided by the Evaluation Team and the PRG.

The cohealth Health Concierge program that operated in Melbourne, Australia from July 2020 to 30 June 2022 rapidly disseminated up-to-date information and increased access to services for largely CALD communities during a rapidly changing pandemic response. Concierges may have contributed to high community uptake of COVID-19 testing and vaccination. CHW models such as this have potential to inform and reassure end-users as long as there is a mechanism to ensure the program can remains responsive to end-users needs. Furthermore, such models may promote protective behaviours in pandemic and non-pandemic settings, with CHWs benefiting from regular public health updates and ongoing supervision.

Data availability

The datasets generated during and analysed for this study are not publicly available due to privacy restrictions.

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Acknowledgements

The authors would like to acknowledge: all participants who contributed their time and effort to this evaluation study; the Project Reference Group members who guided this evaluation; cohealth, the Victorian Government Department of Families, Fairness and Housing, the MARG Foundation and Foundation House, who supported us in participant recruitment; and the data collectors who recruited, surveyed and interviewed participants.

This project was partly funded by a philanthropic donation from the Prior Family Foundation. It was co-funded through the Optimise Study via grants from the Victorian Government COVID-19 Victorian Consortium Clinical/Public Health Pillar, the Macquarie Group and the Burnet Institute.

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Jane Oliver, Ruqiyo Hussein, Peta Edler, Nicole Allard & Katherine B. Gibney

Murdoch Children’s Research Institute, Melbourne, VIC, 3052, Australia

Jane Oliver, Jessica Kaufman & Margie Danchin

Centre for Health Policy, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, 3000, Australia

Angeline Ferdinand

North Melbourne Resident Action Group, Melbourne, VIC, 3052, Australia

Awil Hussein

cohealth, Melbourne, VIC, 3066, Australia

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Contributions

JO– oversaw collection of survey and interview data, designed the analysis, cleaned and analysed the data, interpreted findings, and wrote up findings. AF– designed the analysis, interpreted findings, critically reviewed successive versions of the manuscript. AH - interpreted findings, advised on dissemination strategies, critically reviewed successive versions of the manuscript. RH– collected survey and interview data, interpreted findings, advised on dissemination strategies, critically reviewed successive versions of the manuscript. JK– designed the analysis, interpreted findings, advised on dissemination strategies, critically reviewed successive versions of the manuscript. PE– designed and performed the survey data analysis, interpreted findings, critically reviewed successive versions of the manuscript. NA - designed the analysis, interpreted findings, critically reviewed successive versions of the manuscript. MD - designed the analysis, interpreted findings, critically reviewed successive versions of the manuscript. KBG– conceived of, and designed, the analysis, interpreted findings, critically reviewed successive versions of the manuscript.

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Ethics approval was provided by the University of Melbourne Human Research Ethics Committee (HREC#: 2022-23327-24754-4). All participants provided informed written consent to participate. All experiments were performed in accordance with relevant guidelines and regulations.

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Supplementary Material 1: Appendix 1:

Health Concierge Form 2.0

Supplementary Material 2: Appendix 2:

Supplementary material 3: appendix 3:.

Resident Interview Guide

Supplementary Material 4: Appendix 4.

cohealth Health Concierge Interview Guide

Supplementary Material 5: Appendix 5.

cohealth Staff Interview Guide

Supplementary Material 6: Appendix 6.

Interview Guide for other stakeholders

Supplementary Material 7: Supplementary Table 1.

Ordinal regression identifying factors that predict a one-category increase in score assigned

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Supplementary Material 8: Figure S1.

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Supplementary Material 9: Figure S2.

Locations of the included states, Melbourne, Victoria, Australia

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Oliver, J., Ferdinand, A., Hussein, A. et al. Evaluating a peer-to-peer health education program in Australian public housing communities during the COVID-19 pandemic. BMC Health Serv Res 24 , 250 (2024). https://doi.org/10.1186/s12913-024-10627-7

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Areas of academic research with the impact of COVID-19

Abid haleem.

a Department of Mechanical Engineering, Jamia Millia Islamia, New Delhi, India

Mohd Javaid

Raju vaishya.

b Department of Orthopaedics, Indraprastha Apollo Hospital, Sarita Vihar, Mathura Road, 110076 New Delhi, India

S.G. Deshmukh

c Department of Mechanical Engineering, Indian Institute of Technology Delhi, New Delhi, India

Coronavirus (COVID-19) endemic is growing exponentially in the whole world. Researchers, technologists, doctors and other healthcare workers are working day and night on the development of vaccine and medicinesto control and treat this virus. SARS-CoV-2 is the name of the virus responsible for causing COVID-19 disease, which is highly infectious and lethal.With exponentially increasing infections, proportionate fatalities are being reported both from developed and under developed countries. As of today, more than one million people across the world have been reported infected with this virus, and more than 65,000 people have died of this disease. Hence, there is an urgent requirement for conducting academic research on several aspects of this highly contagious disease, to find effective means of containment and treatment of the disease, for now, and in future. We have identified some opportunities for academic research related to COVID-19 and have also provided suggestions to contain, prevent and treat this viral infection.

The SARS-CoV-2 virus has significantly affected the health, economy, and socio-economic fabric of the global society. The costs involved in the containment and treatment of this infectious disease are exorbitantly high, which even the wealthiestand developed countries are finding it difficult to sustain. COVID-19 pandemic has severely impacted the crude, stock market, gold and metals and almost all areas of the global market [ 1 ]. Large research laboratories and corporate houses are working with a high speed to develop medicines and vaccines for the prevention and treatment of this dreaded disease. To deal with these current health management challenges, we need a comprehensive understanding of the effect on the health system, global business, and culture. COVID-19 was declared a pandemic by the WHO on 11th March 2020 [ 2 ]. COVID-19 has become an international emergency in a short period and will have long-lasting effects. There is an urgent need to identify and study the areas of academic research which will be impacted by COVID-19 [ 3 ].

1. Research objectives

This manuscript highlights potential areas of academic research which are likely to be impacted by COVID-19. The main objectives of this paper are to provide awareness and to identify the research areas related to COVID-19. It may help improve the understanding of this disease and describe the psychological impacts of this pandemic and how these could change as the disease spreads.

2. Current limitations and gaps in the knowledge of Coronavirus and its effects

It appears the Coronavirus is zoonotic and originated in China. Scientists have not yet been able to identify the animal source of the infectious agent and have not determined whether a persistent animal reservoir of the infectious agent exists. It is also unclear whether SARS, like influenza, is a seasonal disease that would have receded on its own. It remains to be seen whether it will reemerge on a seasonal basis, and if so, how virulent future manifestations would be. The answers to these questions would undoubtedly advance the world's ability to predict and prepare for a resurgence of COVID-19.

3. Significant research areas on COVID-19

COVID-19 has disrupted the economies and the lives of individuals around the world. There are many areas of research needed regarding COVID-19 [ [4] , [5] , [6] ]. Table 1 identifies significant research areas which be profoundly impacted by this pandemic. We need to undertake extensive research on these areas.

Major research areas which will be impacted by COVID-19.

Extensive research is required for the development of a vaccine for the prevention of Coronavirus infection. There is an urgent need for early production and manufacturing of the essential items like personal protective equipment, medicines, and ventilators to combat this pandemic. All measures to keep a social distancing by the public must be ensured by avoiding social-cultural and religious programs and festivals etc. during this pandemic. Along with these, healthcare measures to deal with COVID-19 pandemic, there is also an imminent requirement for theresearch to improvethe global economy, which has taken a tremendous beating and is unlikely to recover in the near future [ 7 , 8 ].

4. Conclusion

COVID-19 pandemic is a public health emergency of international concern.It has posed new challenges to the global research community. With the help of academic research, there is a need for a better understanding of the COVID-19 and its socio-economic ramifications on society. The future research will be multi-disciplinary and trans-national.We see a new wave of research in the biological and the medical sciences for the well-being of the civilization.

Declaration of competing interest

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  • Published: 21 February 2024

Making cities mental health friendly for adolescents and young adults

  • Pamela Y. Collins   ORCID: orcid.org/0000-0003-3956-448X 1 ,
  • Moitreyee Sinha 2 ,
  • Tessa Concepcion 3 ,
  • George Patton   ORCID: orcid.org/0000-0001-5039-8326 4 ,
  • Thaisa Way 5 ,
  • Layla McCay 6 ,
  • Augustina Mensa-Kwao   ORCID: orcid.org/0000-0001-8136-6108 1 ,
  • Helen Herrman 7 , 8 ,
  • Evelyne de Leeuw 9 ,
  • Nalini Anand 10 ,
  • Lukoye Atwoli 11 ,
  • Nicole Bardikoff 12 ,
  • Chantelle Booysen   ORCID: orcid.org/0000-0001-7218-8039 13 ,
  • Inés Bustamante 14 ,
  • Yajun Chen 15 ,
  • Kelly Davis 16 ,
  • Tarun Dua 17 ,
  • Nathaniel Foote 18 ,
  • Matthew Hughsam 2 ,
  • Damian Juma 19 ,
  • Shisir Khanal 20 ,
  • Manasi Kumar   ORCID: orcid.org/0000-0002-9773-8014 21 , 22 ,
  • Bina Lefkowitz 23 , 24 ,
  • Peter McDermott 25 ,
  • Modhurima Moitra 3 ,
  • Yvonne Ochieng   ORCID: orcid.org/0000-0002-9741-9814 26 ,
  • Olayinka Omigbodun 27 ,
  • Emily Queen 1 ,
  • Jürgen Unützer 3 ,
  • José Miguel Uribe-Restrepo 28 ,
  • Miranda Wolpert 29 &
  • Lian Zeitz 30  

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Urban life shapes the mental health of city dwellers, and although cities provide access to health, education and economic gain, urban environments are often detrimental to mental health 1 , 2 . Increasing urbanization over the next three decades will be accompanied by a growing population of children and adolescents living in cities 3 . Shaping the aspects of urban life that influence youth mental health could have an enormous impact on adolescent well-being and adult trajectories 4 . We invited a multidisciplinary, global group of researchers, practitioners, advocates and young people to complete sequential surveys to identify and prioritize the characteristics of a mental health-friendly city for young people. Here we show a set of ranked characteristic statements, grouped by personal, interpersonal, community, organizational, policy and environmental domains of intervention. Life skills for personal development, valuing and accepting young people’s ideas and choices, providing safe public space for social connection, employment and job security, centring youth input in urban planning and design, and addressing adverse social determinants were priorities by domain. We report the adversities that COVID-19 generated and link relevant actions to these data. Our findings highlight the need for intersectoral, multilevel intervention and for inclusive, equitable, participatory design of cities that support youth mental health.

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More than a decade ago, Galea posed the question “Can we improve mental health if we improve cities?” 4 . In the past two centuries, urbanization has shaped landscapes and lives, making it the “sentinel demographic shift” of our times 4 . The relationships between mental health status and the social, cultural and physical environment have been explored for at least as long; nineteenth-century researchers proposed environmental exposures as possible explanations of ‘insanity’ 5 . Faris and Dunham’s classic 1930s study 6 linked social disorganization and unstable communities to mental disorders. Two decades later, Leonard Duhl sought to create healthy societies through liveable cities, informing the World Health Organization’s Healthy Cities initiative 7 , 8 . The question remains pertinent today even as we recognize the multiple and complex forces that shape mental health 9 . Today we understand that urban environments influence a broad range of health outcomes for their populations, positively and negatively, and this impact is manifested unequally 10 . Opportunities for education and connection exist for some, whereas rising levels of urban inequality, violence, stressful racial or ethnic dynamics in urban neighbourhoods, exposure to environmental toxins, lack of green space, inadequate infrastructure and fear of displacement increase risk for poor mental health and disproportionately affect marginalized groups 11 . Disparate outcomes also pertain to distinct developmental stages, and the mental health of adolescents and young adults is particularly vulnerable to urban exposures.

Adolescents, youth and urban mental health

Young people under the age of 25 are the demographic group most likely to move to cities for educational and employment opportunities, and by 2050 cities will be home to 70% of the world’s children 3 . Cities concentrate innovation 3 and have long been considered the consummate source of skills, resources and talent 12 . They offer greater opportunities for health and economic development, education, employment, entertainment and social freedoms (that is, the ‘urban advantage’), but rapid urbanization also deepens disparities and exposes individuals to considerable adversity, placing their mental health at risk 13 . In fact, most evidence points to urban living as a risk factor for poorer mental health, yielding increased risk for psychosis, anxiety disorders and depression 1 , 2 . Adolescence and young adulthood, specifically, encompass a critical period of risk for the incidence of mental disorders: an estimated half of mental disorders evident before age 65 begin in adolescence and 75% begin by age 24 (ref.  14 ). Mental disorders are the leading causes of disease burden among 10–24-year-olds worldwide 15 , responsible for an estimated 28.2 million disability-adjusted life years globally, with 1 disability-adjusted life year being equivalent to a healthy year of life lost to the disability caused by mental disorders. Public awareness of these issues rose as the incidence of mental disorders and suicide increased in some countries among adolescents and young adults during the coronavirus pandemic 16 , 17 . Urban environments probably have a role in these processes.

Fundamental to adolescents’ growth and development are their interactions with the complex urban environment: physical, political, economic, social and cultural 18 . Adolescents have a heightened sensitivity to context and social evaluation, and a stronger neural response to social exclusion, as well as to threat and reward stimuli 19 , and it is plausible that they may be particularly sensitive to social and environmental cues in the urban context, such as discrimination or violence. Discriminatory policies and norms are entrenched in many of the institutions with which young people interact (for example, schools, housing, justice and policing), and minoritized youth may experience the emotional and mental health consequences 20 . In fact, in settings of structural inequality (for example, high neighbourhood poverty and unemployment), young people are at greater risk for low self-efficacy and feelings of powerlessness and depression 21 . Social cohesion and collective efficacy can reduce the effects of concentrated disadvantage and nurture social and emotional assets among young people, families and their networks 21 .

At present, the world’s largest population of adolescents and young adults so far is growing up amid the sequelae of a tenacious pandemic, rapid population growth in urban centres and increasing urbanization, demanding an urgent response to support youth mental health 22 . Investing in adolescent well-being is said to yield a triple dividend through actions that reduce mortality and disability in adolescence, prolong healthy life in adulthood, and protect the health of the next generation by educating and strengthening the health of young parents 23 . Interventions in urban settings that align with developmental needs of adolescents and young adults could remediate insults from early life and establish healthy behaviours and trajectories for adult life 19 , 24 , potentially averting chronic conditions such as human immunodeficiency virus (HIV) and the associated mental health, social and physical sequelae 25 . In fact, investment in a package of adolescent mental health interventions can yield a 24-fold return in health and economic benefits 26 . At the societal level, shaping the aspects of urban life that influence youth mental health—through services, social policies and intentional design—could have an enormous impact 4 . Proposals for ‘restorative urbanism’ that centre mental health, wellness and quality of life in urban design may move cities in the direction of moulding urban environments for better adolescent health 27 , 28 . Young people, who contribute to the creativity of urban environments and drive movements for social change 29 , have a central part to play in this transformation.

Mental Health Friendly Cities, a global multi-stakeholder initiative led by citiesRISE, mobilizes youth-driven action and systems reform to promote and sustain the mental health and well-being of young people in cities around the world 30 , 31 ( Supplementary Information ). To guide transformative actions that will enable cities to promote and sustain adolescent and youth mental health, we studied global priorities for urban adolescent mental health. One aim of this study is to contribute data-driven insights that can be used to unite several sectors in cities to act within and across their domains in favour of mental health promotion and care that is responsive to the needs of young people. To that end, we administered a series of linked surveys that permitted the influence of ideas from young people and multidisciplinary domain experts through an anonymous sequential process, following established methods for research priority setting 32 .

Framework and top-ranked recommendations

To determine the elements of an urban landscape that would support mental health for adolescents and youth and would amplify their voices, we recruited a panel of 518 individuals from 53 countries to participate in a series of three digitally administered surveys that began in April 2020 (Table 1 ). Figure 1 shows the panel participation at each round. In survey 1, panellists responded to the open-ended question: “What are the characteristics of a mental health-friendly city for young people?”. Analysis of survey 1 data produced 134 statements about mental health-friendly cities for young people ( Methods ). In survey 2, participants selected their preferred 40 of the 134 statements. They were also presented with a second question related to the influence of the COVID-19 pandemic on their ideas about youth well-being in cities. In survey 3, we categorized survey 2 statements by socioecological domains (Fig. 2 ) and asked panellists to rank-list their preferred statements in each domain. Before ranking, panellists were required to choose one of three framings that informed their selected ranking: immediacy of impact on youth mental health; ability to help youth thrive in cities; and ease or feasibility of implementation.

figure 1

The composition of the project leadership structures, sample recruitment and participation by each survey round are shown below. We invited 801 individuals to participate in the survey panel through recommendations and direct invitations from advisory board members. Participants recruited through snowball sampling received the Research Electronic Data Capture (REDCap) link ( n  = 24). Individuals who gave informed consent in REDCap were deemed to have accepted the survey panel invitation. S1, survey 1; S2, survey 2; S3, survey 3.

figure 2

The socioecological model with six levels (personal, interpersonal, community, organization, policy and environment) that are used to categorize the characteristics of a mental health friendly city.

We present the findings of the third survey within a socioecological model (Figs. 3 – 5 ) because of this model’s relevance to the combination of social and environmental exposures in an urban setting and their interaction with the developing adolescent 33 . Bronfenbrenner’s model begins by recognizing that young people’s personal experiences and development are shaped by their interactions with the people around them 34 ; that is, they react to and act on their immediate environment of familial and peer relationships (microlevel). These interpersonal relationships are also influenced by neighbourhood and community dynamics and exposure to institutions and policies (mesolevel). These, in turn, are nested within the organizational, political, historical, cultural (for example, values, norms and beliefs) and physical environments (macrolevel) whose interplay directly or indirectly affects the adolescent’s mental health and well-being. A high court ruling (policy environment) could have direct or indirect effects on the community, household and personal well-being of a young person seeking asylum. The socioecological framework encompasses the dynamic relationships of an individual with the social environment.

figure 3

Mean ranks and standard deviations (s.d.) values for each mental health-friendly city (MHFC) characteristic are reported grouped by socioecological level and three framings described in the Analysis: immediacy of impact; ability to help youth thrive in cities; and ease or feasibility of implementation. Overall ranks (along with mean and s.d. values) for the total sample are reported. n values in bold represent the number of participants responding for each domain; the percentages in bold represent the percentage of respondents per domain. The number and percentage of the sample that assigned the highest rank for each characteristic are also reported (column 2). The colour continuum from light blue to dark blue shows the highest ranked means in the lightest shades and the lower ranks in darker blue.

figure 4

See the caption of Fig. 3 for details.

figure 5

See the caption of Fig. 3 for details. LGBT+, people from sexual and gender minorities.

The characteristics

We grouped 37 city characteristics across 6 socioecological domains: personal, interpersonal, community, organizational, policy and environmental. Figures 3 – 5 show the mean ranking for each framing and the total mean ranking averaged across frames. We show, for each characteristic statement, the number and percentage of panellists who ranked it highest. The five characteristics in the personal domain centre on factors that enable healthy emotional maturation for young people, future orientation and self-reflexivity. Most panellists (53%) ranked these characteristics according to immediacy of impact on youth mental health in cities, and mean rankings were identical to those linked to ability to help youth thrive in cities. The characteristic that describes prioritizing teaching life skills, providing opportunities for personal development and providing resources that allow young people to flourish rose to the top mean rank for each frame and was also ranked first in this domain by the largest number of panellists ( n  = 93). Notably, the characteristic that describes preparing youth to handle their emotions and overcome challenges was ranked first by 62 panellists, although its mean rank was much lower.

Characteristics in the interpersonal domain refer to young people’s interactions with others in the environment. Prioritized characteristics in this domain centred on relationships marked by acceptance and respect for young people and noted the value of intergenerational relationships. The top-ranked characteristic emphasized age friendliness and interactions that value the feelings and opinions of young people as well as safe and healthy relationships. In this domain, ranked means for characteristics framed according to immediacy of impact on youth mental health and ability to help youth thrive were the same for the top two characteristics. Notably, the two highest-ranked means for ease of implementation focused on opportunities for safe and healthy relationships and strengthening intergenerational relationships.

Young people’s intrapersonal experiences and interpersonal relationships are nested within a system of community and organizational relationships. Study participants prioritized access to safe spaces for youth to gather and connect among the three characteristics in the domain of community, and rankings were identical for each framing. At the organizational domain, two characteristics shared high mean rankings: employment opportunities that allow job security and satisfaction and a responsive and supportive educational system. Health-care services and educational services were the organizations most frequently referenced in relation to youth mental health. Whereas employment opportunities ranked first in terms of feasibility of implementation, provision of youth-friendly health services ranked first for immediacy of impact on youth mental health. With the exception of the community and organizational domains, more panellists chose to frame their responses in terms of immediacy of impact on youth mental health.

Of the four statements in the policy domain, the design and planning of cities with youth input and gender sensitivity ranked highest overall and was most frequently ranked first by panellists (30.68%). Promoting democratic cooperation and equal opportunity and anti-discrimination in all institutions received the highest mean rank for feasibility of implementation.

The sixth socioecological domain lists 13 characteristics related to the social, cultural and physical environments. Addressing adverse social determinants of health for young people had the highest overall ranked mean; however, normalizing youth seeking mental health care and addressing service gaps ranked first when framed by feasibility of implementation and immediacy of impact. Having access to affordable basic amenities was most frequently ranked first in this domain by panellists, but panellist preferences were distributed across the list.

COVID-19 and urban youth well-being

Our data collection began in April 2020 during the COVID-19 pandemic, and by survey 2 (August 2020), most countries were experiencing the pandemic’s public health, social and economic effects. In light of this, we added an open-ended survey question to which 255 participants responded “How has the COVID-19 pandemic changed your ideas about the wellbeing of young people in cities?” ( Methods ). Most respondents reported changes in perspective or new emphases on inequities as determinants of youth well-being and mental health, whereas nine reported that COVID-19 did not change their ideas. For one such respondent (in the >35 years age category), the pandemic merely confirmed the powerful effect of social vulnerabilities on risk and outcomes during an emergency: “COVID-19 has not changed my ideas about the wellbeing of young people in cities. I found that the young people in cities who did well during the lockdown period and the difficult period of the pandemic were those who were already doing well in terms of a rich social network, good interpersonal relations with family and friends, enjoyable work life, a close religious network, membership [in] a young people’s club so that they were able to stay connected via social media. Those who had access to food and essential commodities and those who knew they would return to school or work after the pandemic. Those who had access to good living conditions and some space for recreation also did well. ... The impact of COVID19 was felt much more by those with existing mental health conditions, living in crowded slums, poverty, unemployment, who were uncertain about the next step”.

Respondents highlighted losses young people experienced as a result of the pandemic. These included loss of the city as a place of opportunity; loss of jobs, familial and individual income, and economic stability; loss of a planned future and loss of certainty; loss of rites of passage of youth; loss of access to friends, social networks and social support; loss of access to quality education and to health care, especially mental health care and sexual and reproductive health services; loss of opportunities for psychological and social development; and loss of loved ones who died from COVID-19. We summarize the qualitative findings according to the socioecological framework. We present sample quotes in Table 2 , along with the age category of the respondents (18–24, 25–35 and >35) and actions for cities to take.

Policy and environment

Governance and equity.

Freedom from discrimination and the value of equity were listed among the mental health-friendly city characteristics; however, respondents pointed out the dearth of equity that COVID-19 unveiled (see the first quote in Table 2 ).

Respondents observed that policy responses to COVID-19, including mandated curfews and quarantines, shifted the social and economic environment of cities. Young people and their families lost economic opportunities, and cities also became less affordable during the pandemic. Participants explained that poverty and job loss worsened young people’s mental health and well-being and exposed youth to more risk factors because they needed to “hustle or work to place food on the table”. The loss of jobs also deprived youth of hope and underlined the economic inequities that some felt marked their generation more than previous ones. One participant (18–24) reported “Before, I used to think youths need someone who can understand them, empathize with them, but looking at the current scenario, I feel youths need security and a hopeful future too”. In some settings, these economic shifts resulted in an exodus from cities. A respondent (18–24) observed “Cities have always attracted young people but since the pandemic started the cost of living has gone from being a barrier to being another factor in encouraging young people to leave”.

Urban built environment

For those who remained in the city, the urban built environment could also offer respite from pandemic-related restrictions in mobility when green spaces and other open spaces were accessible. Participants alluded to cramped urban housing, crowded slums and poor housing infrastructure as stressors that the availability of safe public spaces alleviated. Green space in particular provided solace for young people. A participant (18–24) responded “It’s difficult when you’re confined to the limited space especially when you’re not closer to nature. Negative thoughts get you one way or another even if you try your best. Pandemic has caused more depression I reckon among the youths”. Accessible green space was highlighted as a need and an area for investing effort and policy change (Table 2 ). A desire for clean, youth-friendly green space for safe gathering and recreation was contrasted with unplanned land use and confined spaces, the latter of which some participants linked to greater risks for young people.

Community and organizations

Respondents reported diminished access to education and health care, and a disregard of young people’s needs by decision-makers (Table 2 ). Some responses criticized the lack of forethought before the pandemic to budget for and provide supportive learning environments for youth of all socioeconomic strata. The closure of schools generated stress for young people with the disruption of routines and opportunities to socialize. The pandemic generated greater uncertainty about job opportunities and future trajectories. At the same time, the pandemic brought opportunities to position youth as either contributors and leaders or detractors from community life. Young people reflected on how they experienced inclusion, empathy and exclusion, as well as opportunity for leadership. One respondent (25–35) commented “Our worlds are changing and with it many of our expectations about our education, work, personal interactions and relationships. Instead of being met with understanding, we are collectively positioned as transgressors of social distancing in a way that fails to understand that we are often incredibly vulnerable in this new world and left exposed by lack of infrastructure, service provision and support”.

A respondent (18–24) noticed possibilities for involving young people in responses that could mitigate their numerous losses: “Given the opportunities and resources, young people can be a carrier of change and wellbeing if adults trust them enough to be”.

Interpersonal domain

Getting through difficult times required interpersonal supports: connectedness through in-person encounters in safe spaces, complemented by digital interactions. Multiple respondents emphasized the relationship between social isolation and poor mental health among city youth during the pandemic, noting the difficulty of making meaningful connection during a time of physical isolation. Two young respondents (18–24) said the well-being of young people was linked to being “in a group of people”, which provides “safety and unity”, and to “inclusion, activity, and interpersonal relationships”. Space repeatedly emerged as a theme, as a conduit to facilitate social connection for young people without risk of COVID-19 transmission, violence, sexual abuse or exposure to drug use. Some participants called for greater investment in creating strong, safe virtual communities for young people; however, although participants identified virtual spaces as a resource for mental health support, a young panellist (18–24) remarked of social media and technology that “It isolated people, even though we have … ways of staying connected 24/7, we still feel lonely.”

Consistent with the lead mental health-friendly city characteristic in the personal domain (Figs. 3 – 5 ), the pandemic prompted realization of the need for personal skills development to support youth mental well-being. Some respondents expressed concern about the loss of social skills among young people as a result of confinement and an 18–24-year-old commented “… Youths are in that stage where they need to be equipped with skills to promote positive mental wellbeing”. Another young person (18–24) remarked “Most of us do not really have the capacity and necessary skills to support each other when it comes to mental health”. Participants described the importance of being prepared for unpredictable circumstances and enabling youth to “manage themselves, their emotions, and wellbeing”.

Pandemic-related gains

In some cases, the pandemic brought positive experiences for young people, including more time for self-reflection and discovery, engaging in healing practices, more opportunities to connect with friends, and overall, a greater societal and individual focus on strengthening mental health. A participant (25–35) referred to young people: “They are more conscious about health and their wellbeing by reducing workload and connecting with nature”. Others believed the pandemic revealed young people’s capacity to adapt and to consider the needs of their elders. Some viewed the social justice uprisings that occurred in many countries as a positive vehicle for change and cooperation with others. Changing these conditions would require longer-term solutions: strengthening urban infrastructure and addressing the underlying drivers of inequity. Another participant (>35) lauded the power of youth activism: “… the pandemic has shown us that the resilience of youth is great, as well as the commitment and solidarity with their communities through volunteering, advocacy and youth mobilization”.

Our study convened a multinational and multidisciplinary panel of researchers, practitioners, advocates and young people to identify the characteristics of a mental health-friendly city for youths. The characteristics are distributed among six socioecological domains (Figs. 3 – 5 ) that encompass the personal development of young people, supportive educational systems, people-centred health care, a built environment responsive to the needs of young people, and equity-focused policy-making and governance. Within each of these domains, the characteristics we identified are associated with an evolving evidence base linked to youth mental health outcomes and to potential policy intervention.

Intrapersonal characteristics in our list underline the centrality of enabling young people to cultivate skills to manage their interior lives. The targets of such skills-building activities align with proposed ‘active ingredients’ of mental health interventions, such as intervention components related to mechanisms of action or clinical effects on depressive or anxiety symptoms 35 . Examples include affective awareness skills that enable young people to differentiate and describe emotions 36 and emotion regulation skills to increase and maintain positive emotions 37 . Youth-friendly mental health and educational services, a priority theme at the community level of the framework, could support the intrapersonal realm by deploying a variety of interventions for self-control that benefit adolescent and young adult academic, behavioural and social functioning 38 . Such interventions can also be implemented in earlier childhood educational settings through integration into the curriculum or through other community-based medical or social service organizations 39 . Interventions implemented in selected high-income settings include Promoting Alternative Thinking Strategies 40 , the Incredible Years 41 and Family Check-up 42 . For young adults, interventions that convey skills to alleviate common psychological problems such as procrastination, perfectionism, low self-esteem, test anxiety and stress could potentially reduce the prevalence of specific mental health conditions while possibly providing acceptable and non-stigmatizing options for care 43 , 44 .

Our data suggest that a defining theme of any mental health-friendly city for youth is the quality of young people’s social fabric and the city’s ability to provide young people with the skills, opportunities and places required to build and maintain healthy social relationships with their peers, across generations, and as members of a community. The relationships of concern in the interpersonal realm have intrinsic value for healthy adolescent and youth development, promoting well-being 45 and prevention of depression 46 , 47 . Panellists also linked opportunities to socialize and build social networks to the availability of safe spaces, the top-ranked priority in the community domain. Achieving safety necessitates equitable and violence-free institutions and cities 48 , a priority that panellists ranked first for ease of implementation in the policy domain. Thus, policies and legislation are required that reduce neglect, bullying, harassment, abuse, censorship, exposure to violence and a wide range of threats towards young people, from homelessness to crime to intimidation by officials 48 , 49 .

Exposure to community violence and household violence consistently worsens mental health outcomes for youth 50 , 51 , 52 , 53 ; successful reduction of urban violence should be prioritized. Equity-focused responses to safety needs should include reducing discriminatory physical and structural violence against young people based on race, ethnicity, gender, sexuality or mental health status, which place youth at risk of harmful exposures: rape or trafficking of adolescent girls or police killings of North American Black youth. To create urban spaces in which young people can experience safety, freedom and belongingness requires approaches that actively prevent discrimination 54 and that consider young people’s multiple identities in the design of institutional as well as outdoor spaces. Women-only parks create greater security for girls and young women and potentially more positive social interaction in some settings 55 .

The benefits of green space, measured as self-satisfaction for adolescents, are linked to greater social contact (for example, more close friends), underscoring space as a conduit for social connection 55 . The advantages of healthy urban spaces for adolescents have emerged not only in health sciences research but also in allied fields such as urban design and sociology 27 , 56 , 57 . Urban spaces with opportunities for active commute options to and from school are associated with increased physical activity and environmental supportiveness 58 . Similarly, the presence of community spaces, such as town centres, is associated with improved social connectedness and sense of belonging 59 .

The critical importance of social connectedness was reinforced in the COVID-19 responses. Yet, in many cities the pandemic eliminated spaces that foster urban conviviality, often with lasting effects 60 . Restricted movement and COVID-19 transmission risk associated with public transport may have contributed to greater stress for urban dwellers and ongoing reluctance to use these services 61 . Such factors contribute to social isolation, which may persist in the near term. Consistent with our COVID-19 data, responses from a sample of Australian youth identified social isolation, interrupted education and work, and uncertainty about the future among the primary negative effects of COVID-19 pandemic 62 . In several studies, loneliness increased the risk of mental health conditions among young people during prior epidemics; of relevance to the COVID-19 pandemic, the duration of loneliness predicted future mental health problems 63 .

Analysis of our survey 2 data revealed differences in the priorities of young participants (18–24 and 25–35) compared with panellists over age 35. This discrepancy could have implications for urban decision-makers whose plans to implement positive actions on behalf of young people may not align with what is most salient for youth. Thus, youth involvement in policy development is even more crucial. Soliciting youth perspectives about what supports their mental health based on their personal experiences could simplify and improve interventions intended for them 64 . Several actions could facilitate meaningful youth engagement in governance: encourage collaboration between governments and youth organizations to co-create and co-lead national action plans; implement mechanisms within global governance organizations for youth consultation at local, national and international levels; require inclusion of young people on relevant conference agendas; and improve access to funding for youth-led organizations 65 , 66 .

Notably, the themes of equity and elimination of discrimination due to race, gender, sexual orientation and neurodiversity arose frequently in the responses to the survey and the COVID-19 question, as did the adversities to which minoritized groups are vulnerable (for example, community violence, police violence and bullying; Figs. 4 and 5 ). A city that is free of discrimination and racism ranked first among policy responses with immediacy of impact on the mental health of youth—even though no statements proposed dismantling systems of oppression that underlie racism and discrimination, as one respondent noted (Fig. 4 ). Globally, racism, xenophobia and other forms of discrimination increase mortality and harm the mental health of affected groups through stress-related physiological responses, harmful environmental exposures and limited access to opportunities and health services 20 , 67 , 68 , 69 . Embedded racist and xenophobic norms, policies and practices of institutions—including those that govern educational, labour and health care systems—yield racialized outcomes for young people around the world (for example, high incidence of HIV infection among adolescent girls in southern sub-Saharan Africa) 20 . To disrupt these forces requires multiple approaches, including recognition and remedy of historical injustices, the activism of social movements committed to change, and implementation of legal frameworks based in human rights norms 70 .

When participants ranked characteristics for ease of implementation (Figs. 3 – 5 ), they coalesced around a broad set of factors demonstrating the need for collaboration across urban sectors (for example, normalizing seeking mental health care, promoting democratic cooperation and equal opportunity, and creating employment opportunities and progressive educational systems). This need for cooperation is perhaps most apparent for actions that increase equity. Successful cooperation requires a clear, shared vision and mission, allocation of funding in each sector, diversity of funding sources, distributed decision-making and authority across sectors, and policies that facilitate collaboration 71 . However, well-intentioned cross-sectoral responses to urban needs may inadvertently increase inequities by designing programmes influenced by market forces that magnify environmental privilege (that is, unequal exposure to environmental problems according to social privilege) 54 . Examples include gentrification and development that use land to create green spaces but further dislocate and marginalize communities in need of affordable housing 54 . Implementing community- and youth-partnered processes for urban health equity policy co-creation could yield unified agendas and help to circumvent inequitable outcomes 54 , 72 . A mental health-friendly city must be positioned to support, integrate and enable the thriving of marginalized and vulnerable young people of the society, who should be involved in its governance.

Strengths and limitations

Our study has several strengths. First, this priority-setting study yielded a rich dataset of recommended characteristics of a mental health-friendly city for young people from a globally diverse panel of more than 480 individuals from 53 countries. Second, we welcomed expertise from participants with roles relevant to urban sectors: researchers, policymakers and practice-based participants, and we engaged young people in the study advisory board and as study participants, capitalizing on their lived experience. Third, we captured information about how the COVID-19 pandemic influenced participants’ ideas about urban adolescent mental health. Fourth, to our knowledge, this is the first study that brings together a large and multidisciplinary set of stakeholders concerned for cities (for example, urban designers) and for youth mental health (for example, teachers and health professionals) to identify priorities for intersectoral action.

Our study also has several limitations. First, the participants recruited do not reflect the full social and economic diversity of urban populations whom city governments and decision-makers must serve. Our decision to use a web-based format following standard health research priority-setting methods required tradeoffs. We sought disciplinary, age and geographic diversity; however, our sample does not represent the most marginalized groups of adolescents or adults. Rather, the recruitment of academics, educators, leaders and well-networked young people through an online study probably minimizes the number of participants living in adversity. Although we also recruited young people who were not necessarily established experts, many were students or members of advocacy or international leadership networks and were not likely to exemplify the most disadvantaged groups. We risk masking the specific viewpoints or needs of marginalized and at-risk young people. However, we are reassured by the prominence of equity as a theme and the call to address social determinants of health. Second, it is possible that participants recruited through the authors’ professional networks may be more likely to reflect the viewpoints of the advisory committee members who selected them, given collaborative or other professional relationships. This may have shaped the range of responses and their prioritization. Third, the aspirational calls for an end to discrimination and inequalities highlighted in our results require confronting long-standing structural inequities both within and between countries. Structural violence frequently maintains these power imbalances. Although we do not view their aspirational nature as a limitation, we note that our study data do not outline the complexity of responses required to address these determinants of mental health or to dismantle discriminatory structures. Fourth, our data present several aggregated characteristics that may require disaggregation as cities contextualize the findings for their settings. Fifth, our network recruitment strategy led to skewed recruitment from some geographic regions (for example, North America and Nepal), which may have biased responses (Extended Data Figs. 1 – 3 ). Extended Data Table 1 shows the similarities and differences in the rankings for Nepal, USA and the remaining countries in survey 3. Additionally, we recruited few 14–17-year-olds. We experienced attrition over the three rounds of surveying, ending with complete responses from 261 individuals from 48 countries, with the greatest loss in participants between surveys 1 and 2 (Table 1 ), among the 14–17-, 18–24- and 25–35-year-old age groups, and among participants from Nepal (Extended Data Fig. 2 ).

Conclusions

We identified a set of priorities for cities that require intervention at multiple levels and across urban sectors. A clear next step could involve convenings to build national or regional consensus around local priorities and plans to engage stakeholders to co-design implementation of the most salient characteristics of a mental health-friendly city for youth in specific cities (Box 1 ). It is likely that many variables (for example, geography, politics, culture, race, ethnicity and sexual identity) will shape priorities in each city. Therefore, essential to equitable action is ensuring that an inclusive community of actors is at the table formulating and making decisions, and that pathways for generating knowledge of mental health-friendly city characteristics remain open. This includes representation of sectors beyond mental health that operate at the intersection of areas prioritized by young people. Preparing for implementation will require avenues for youth participation and influence through collective action, social entrepreneurship and representation in national, regional and community decision-making. Enlisting the participation of youth networks that bring young people marginalized owing to sex, gender, sexual orientation, race, economic status, ethnicity or caste; young people with disabilities; and youth and adults with lived experience of mental health conditions in the design of mental health-friendly cities will help to level power imbalances and increase the likelihood that cities meet their needs.

Action for adolescent mental health aligns well with actions nations should take to achieve development targets, and collective action to draw attention to these areas of synergy could benefit youth and cities. Specifically, supporting the mental health of young people aligns with Sustainable Development Goal 11 (sustainable cities and communities) and the New Urban Agenda that aims to “ensure sustainable and inclusive urban economies, to end poverty and to ensure equal rights and opportunities … and integration into the urban space” 73 , 74 , 75 .

Additionally, the list of mental health-friendly city characteristics presents a starting point for strengthening the evidence base on intervening at multiple levels (for example, individual, family, community, organizations and environment) to better understand what works for which youth in which settings. Cities function as complex systems, and systems-centred research can best enable us to understand how individuals’ interactions with one another and with their environments influence good or poor mental health 76 . Similarly, interdisciplinary inquiry is needed that investigates urban precarity and sheds light on social interventions for youth mental health 77 . New research that tests implementation strategies and measures mental health outcomes of coordinated cross-sectoral interventions in cities could be integrated with planned actions. Innovative uses of data that measure the ‘racial opportunity gap’ can help cities to understand how race and place interact to reduce economic well-being for minoritized young people on their trajectory to adulthood 78 . Even heavily studied relationships, such as mental health and green space, can benefit from new methodologies for measuring exposures, including application of mixed methods, and refined characterization of outcomes by gender and age with a focus on adolescents and youth 79 . Globally, mental health-supporting actions for young people in urban areas have an incomplete evidence base, with more peer-reviewed publications skewed towards North American research 73 .

Designing mental health-friendly cities for young people is possible. It requires policy approaches that facilitate systemic, sustained intersectoral commitments at the global as well as local levels 80 . It also requires creative collaboration across multiple sectors because the characteristics identified range from transport to housing to employment to health, with a central focus on social and economic equity. Acting on these characteristics demands coordinated investment, joint planning and decision-making among urban sectoral leaders, and strategic deployment of human and financial resources across local government departments that shape city life and resources 75 , 81 . This process will be more successful when cities intentionally and accountably implement plans to dismantle structural racism and other forms of discrimination to provide equitable access to economic and educational opportunities for young people, with the goal of eliminating disparate health and social outcomes. The process is made easier when diverse stakeholders identify converging interests and interventions that allow them each to achieve their goals.

Box 1 Considerations for implementing a mental health-friendly city for youth

Considerations for implementing a mental health-friendly city for youth using a structure adapted from UNICEF’s strategic framework for the second decade of life 82 and integrating selected characteristics identified in the study with examples distilled from scientific literature and from project advisory group members. Objectives for implementation along with corresponding examples and selected initiatives are shown.

Youth are equipped with resources and skills for personal and emotional development, compassion, self-acceptance, and flourishing.

Youth develop and sustain safe, healthy relationships and strong intergenerational bonds in age-friendly settings that respect, value and validate them.

Communities promote youth integration and participation in all areas of community life.

Communities establish and maintain safe, free public spaces for youth socializing, learning and connection.

Institutions facilitate satisfying, secure employment; progressive, inclusive, violence-free education; skills for mental health advocacy and peer support.

Policies support antiracist, gender equitable, non-discriminatory cities that promote democratic cooperation and non-violence.

Urban environments provide safe, reliable infrastructure for basic amenities and transportation; affordable housing; access to green and blues space; and access to recreation and art.

Cities minimize adverse social determinants of health; design for safety and security for vulnerable groups; and orient social and built environments to mental health promotion, belonging and purpose.

Use rights-based approaches

Prioritize equity for racially, ethnically, gender, sexually and neurologically diverse young people

Ensure sustained and authentic participation of youth

Schools and other educational settings

Health and social services

Families and communities

Religious and spiritual institutions

Child protection and justice systems

Peer groups

Civil society

Digital and non-digital media

Implementation objectives

Build consensus and contextualize the mental health-friendly city approach at local, regional, national levels

Engage diverse youth in co-design of mental health-friendly city plans

Expand opportunities for youth governance

Enable collaboration among sectors for policy alignment

Engage communities, schools, health services, media for intervention delivery

Legislate social protection policies

Scale interventions to improve economic and behavioral outcomes

Link implementation to achievement of national or international objectives

Selected implementation strategies

Youth co-design and participation: Growing Up Boulder is an initiative to create more equitable and sustainable communities in which young people participate and influence issues that affect them. It is a partnership between local schools, universities, local government, businesses and local non-profit organizations in the USA that has enabled young people to formally participate in visioning processes such as community assessments, mapping, photo documentation and presentations to city representatives 83 .

Engaging schools for interventions: universal school-based interventions for mental health promotion 84 ; linkage to mental health care for school-based programs 85 ; “Whole-school approaches” that engage students and families, communities, and other agencies to support mental health and improve academic outcomes 84 , 86 .

Digital platforms for youth mental health: Chile’s HealthyMind Initiative digital platform launched during the COVID-19 pandemic and provided a one-stop resource for information and digital mental health services. The platform included targeted evidence-based resources for children and adolescents 87 .

Interventions to test at scale: Stepping Stones and Creating Futures is a community-based intervention for intimate partner violence reduction and strengthening livelihoods in urban informal settlements in South Africa that reduced young men’s perpetration of intimate partner violence and increased women’s earning power 88 .

Shared international objectives: support Sustainable Development Goal 11 and New Urban Agenda targets and Sustainable Development Goals 1–6, 8, 10 and 16.

Project structure and launch

This study aimed to identify priorities for creating cities that promote and sustain adolescent and youth mental health. Central to achieving this aim was our goal of engaging a multidisciplinary, global, age-diverse group of stakeholders. As we began and throughout the study, we were cognizant of the risk of attrition, the importance of maintaining multidisciplinary participation throughout the study and the value of preserving the voices of young people. We used a priority-setting methodology explicitly aimed to be inclusive while simultaneously limiting study attrition. To ensure that we were inclusive of the voices of young people and our large and diverse sample, we limited our study to three surveys, which we determined a priori. Our approach was informed by standard methodologies for health research priority setting 32 .

The project was led by a collaborative team from the University of Washington Consortium for Global Mental Health, Urban@UW, the University of Melbourne and citiesRISE. We assembled three committees representing geographic, national, disciplinary, gender and age diversity to guide the work. First, a core team of P.Y.C., T.W., G.P., M.S. and T.C., generated an initial list of recommended members of the scientific advisory board on the basis of their research and practice activities related to adolescent mental health or the urban setting. We sought a multidisciplinary group representing relevant disciplines. The 18-member scientific advisory board, comprising global leaders in urban design and architecture, social entrepreneurship, education, mental health and adolescent development, provided scientific guidance. We invited members of an executive committee, who represented funding agencies as well as academic and non-governmental organizational leadership, to provide a second level of feedback. A youth advisory board, recruited through citiesRISE youth leaders and other global mental health youth networks, comprised global youth leaders in mental health advocacy. A research team from the University of Washington (Urban@UW, the University of Washington Population Health Initiative and the University of Washington Consortium for Global Mental Health) provided study coordination. The study received institutional review board approval at the University of Washington (STUDY00008502). Invitations to advisory groups were sent in December 2019, along with a concept note describing the aims of the project, and committee memberships were confirmed in January 2020. In February 2020, the committees formulated the question for survey 1: “What are the characteristics of a mental health friendly city for young people?”.

Study recruitment

The members of the scientific advisory board, youth advisory board and executive committee were invited to nominate individuals with expertise across domains relevant to urban life and adolescent well-being. The group recommended 763 individuals to join the priority-setting panel; individuals invited to serve on the scientific advisory board, youth advisory board and executive committee were included in panel invitations ( n  = 38). Our goal was to establish a geographically diverse panel of participants with scientific, policy and practice-based expertise corresponding to major urban sectors and related challenges (for example, health, education, urban planning and design, youth and criminal justice, housing and homelessness, and violence). Many of the nominees were experts with whom the core group and scientific advisory board members had collaborated, as well as individuals recruited on the basis of their participation in professional and scientific associations and committees (for example, Lancet Commissions and Series) or global practice networks (for example, Teach for All). Nominees’ names, the advisory member who nominated them, gender, country and discipline were tracked by T.C. We used snowball sampling to recruit participants from geographic regions that were under-represented: an additional 24 people were recruited through referrals. The scientific advisory board and youth advisory board sought to maximize the number of young people participating in the study, and invitations were extended to adolescents and young adults through educational, professional, advocacy and advisory networks. Nominees received an invitation letter by e-mail, accompanied by a concept note that introduced the study, defined key constructs, described the roles of the study advisory groups and provided an estimated study timeline. Youth participants (14–24) received a more abbreviated introductory letter. A link to a REDCap survey with an informed consent form and round 1 question was embedded in the invitation e-mail, which was offered in English and Spanish. Of the 824 individuals invited, 518 individuals from 53 countries provided informed consent and agreed to participate, resulting in a nomination acceptance rate of 62.8%.

Data collection

We administered a series of three sequential surveys using REDCap version 9.8.2. Panellists were asked to respond to the survey 1 question “What are the characteristics of a mental health friendly city for young people?” by providing up to five characteristics and were invited to use as much space as needed. In survey 2, panellists received 134 characteristic statements derived from survey 1 data and were asked to select their 40 most important statements. From these data, we selected 40 most frequently ranked statements. These were presented in the round 3 survey with three redundant statements removed. The remaining 37 characteristic statements were categorized across 6 socioecological domains and panellists were asked to select 1 of 3 framings by which to rank the statements in each domain: immediacy of impact on youth mental health in cities, ability to help youth thrive in cities, and ease or feasibility of implementation. Of individuals who consented to participate, 93.4% completed round 1, 58.5% completed round 2 and 56.2% completed round 3 (Table 1 ).

We added a new open-ended question to survey 2: “How has the COVID-19 pandemic changed your ideas about the wellbeing of young people in cities?”. Panellists were invited to respond using as many characters (that is, as much space) as needed.

Data analysis

Three-survey series.

We managed the survey 1 data using ATLAS.ti 8 software for qualitative data analysis and conducted a conventional content analysis of survey 1 data 89 . Given the multidisciplinarity of the topic and our multidisciplinary group of respondents, we selected an inductive method of analysis to reflect, as simply as possible, the priorities reported by the study sample without imposing disciplinary frameworks. In brief, responses were read multiple times, and characteristics were highlighted in the text. A list of characteristics (words and phrases) was constructed, and we coded the data according to emerging categories (for example, accessibility, basic amenities, career, built environment, mental health services and so on). The analysis yielded 19 broad categories with 423 characteristics. Within each category, characteristics were grouped into statements that preserved meaning while streamlining the list, which yielded 134 characteristic statements. The University of Washington research team convened a 1-week series of data discussions with youth advisers to review the wording of the characteristics and ensure their comprehensibility among readers from different countries. The survey 1 categorized data were reviewed by members of the scientific advisory board, who recommended that using relevant domains to group characteristics would provide meaningful context to the final list. We used IBM SPSS 28.0 for quantitative analyses of data from surveys 2 and 3. In survey 2, we analysed the frequency of endorsement of the 40 characteristics selected by panellists and generated a ranked list of all responses, with the most frequently endorsed at the top. The decision to select 40 characteristics aligned with methods applied in a previous priority-setting exercise 90 and permitted a list of preferred characteristics that could subsequently be categorized according to a known framework, allowing city stakeholders a broad list from which to select actions. We also analysed frequency of endorsement by age categories (18–24, 25–35 and >35). To amplify the viewpoints of younger participants (under age 35), we combined the top 25 characteristic statements of panellists over 35 with the top 26 characteristic statements of participants under 35 to generate a list of 40 statements, including 11 shared ranked characteristics. As noted, we removed three of these statements because of their redundancy. In survey 3, we analysed data consisting of 37 characteristic statements divided across 6 socioecological domains. Characteristics in each domain were ranked according to one of three framings. We calculated mean ranking and standard deviation for characteristics in each framing category per socioecological domain. Mean rankings (with standard deviation) were calculated across framing categories to arrive at the total mean rank per characteristic and they reflect the proportional contribution of each domain. We also calculated the frequency with which panellists ranked each characteristic statement number 1.

Our study methods align with good practices for health research priority setting as follows 32 .

Context: we defined a clear focus of the study.

Use of a comprehensive approach: we outlined methods, time frame and intentions for the results before beginning the study; however, we modified (that is, simplified) the methods for survey 3 to minimize study attrition.

Inclusiveness: we prioritized recruiting for broad representation and maintaining engagement of an inclusive participant group, and methodological decisions were made in service of this priority.

Information gathering: our reviews of the literature showed that a study bringing together these key stakeholders had not been conducted, despite the need.

Planning for implementation: we recognized from the outset that additional convening at regional levels would be required to implement action, and our network members are able to move the agenda forwards.

Criteria: we determined criteria for the priorities (framing: feasibility of implementation, immediacy of impact and ability to help youth thrive) that study participants used and which we believe will be useful for practical implementation.

Methods for deciding on priorities: we determined that rank order would be used to determine priorities.

Evaluation: not applicable; we have not planned an evaluation of the impact of priority setting in this phase of work.

Transparency: the manuscript preparation, review and revisions enable us to present findings with transparency.

COVID-19 qualitative data

We managed the COVID-19 qualitative data using Microsoft Excel and Microsoft Word. We carried out a rapid qualitative analysis 91 . First, the text responses were read and re-read multiple times. We coded the data for content related to expressions of change, no change or areas of emphasis in participants’ perceptions of youth mental health in cities during the pandemic. We focused our attention on data that highlighted changes. We further segmented the data by participant age categories, domains of change and suggested actions, and we assigned socioecological level of changes. We created a matrix using excerpted or highlighted text categorized according to these categories. Three data analysts (P.Y.C., T.C. and A.M.-K.) reviewed the domains of change and identified emerging themes, which were added to the matrix and linked to quotes. The team discussed the themes and came to consensus on assignment to a socioecological level. We prioritized reporting recurring concepts (for example, themes of loss, inequity, green space, isolation and mental illnesses) and contrasting concepts (for example, gains associated with COVID-19) and associated actions 92 .

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

Survey data that support the findings of this study are available from the corresponding author, P.Y.C., on reasonable request. The sharing of data must comply with institutional policies that require a formal agreement (between the corresponding author and the requester) for sharing and release of data under limits permissible by the institutional review board.

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Acknowledgements

We thank M. Antia, S. Talam and J. Vollendroft for contributions to this project; H. Jack for contributions to the manuscript revision; and the survey panellists without whom this work would not have been possible. M.K. was supported in part by funding from the Fogarty International Center (K43 TW010716) and the National Institute of Mental Health (R21 MH124149) of the National Institutes of Health. This study was supported in part by funding to citiesRISE (M.M. and M.H.) from the Rural India Supporting Trust and from Pivotal Ventures. This study was conducted while P.Y.C. was on the faculty at the University of Washington, Seattle. The University of Washington (P.Y.C. and T.C.) received funding from citiesRISE by subcontract. T.D. is a staff member of the World Health Organization (WHO). The content and views expressed in this manuscript are solely the responsibility of the authors and do not necessarily represent the official views, decisions or policies of the institutions with which they are affiliated, including WHO, the US Department of Health and Human Services and the National Institutes of Health.

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Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

Pamela Y. Collins, Augustina Mensa-Kwao & Emily Queen

citiesRISE, New York, NY, USA

Moitreyee Sinha & Matthew Hughsam

Department of Global Health, University of Washington, Seattle, WA, USA

Tessa Concepcion, Modhurima Moitra & Jürgen Unützer

Centre for Adolescent Health, University of Melbourne, Melbourne, Victoria, Australia

George Patton

Dumbarton Oaks, Harvard University, Washington, DC, USA

Centre for Urban Design and Mental Health, London, UK

Layla McCay

Orygen, Melbourne, Victoria, Australia

Helen Herrman

University of Melbourne, Melbourne, Victoria, Australia

Ecole de Sante Publique, Universite de Montreal, Montreal, Quebec, Canada

Evelyne de Leeuw

Fogarty International Center, National Institutes of Health, Bethesda, MD, USA

Nalini Anand

Aga Khan University, Nairobi, Kenya

Lukoye Atwoli

Grand Challenges Canada, Toronto, Ontario, Canada

Nicole Bardikoff

Good South Social Impact Enterprise, Durban, South Africa

Chantelle Booysen

Universidad Peruana Cayetano Heredia, Lima, Peru

Inés Bustamante

Sun Yat Sen University, Guangzhou, China

Mental Health America, New York, NY, USA

Kelly Davis

World Health Organization, Geneva, Switzerland

The TruePoint Center, Boston, MA, USA

Nathaniel Foote

Healthy Brains Global Initiative, Nairobi, Kenya

Damian Juma

Teach for Nepal, Kathmandu, Nepal

Shisir Khanal

Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA

Manasi Kumar

University of Nairobi, Nairobi, Kenya

Sacramento County Board of Education, Sacramento, CA, USA

Bina Lefkowitz

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Peter McDermott

Duke University, Durham, NC, USA

Yvonne Ochieng

University of Ibadan, Ibadan, Nigeria

Olayinka Omigbodun

Pontificia Universidad Javeriana, Bogotá, Colombia

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Contributions

This study was led by a core group, P.Y.C., G.P., M.S. and T.W., who were members of the project’s scientific advisory board and executive committee and part of the group of 32 co-authors (P.Y.C., M.S., T.C., G.P., T.W., L.M., A.M.-K., L.A., N.B., I.B., Y.C., T.D., E.d.L., N.F., H.H., S.K., M.K., B.L., O.O., J.M.U.-R., C.B., K.D., M.H., D.J., M.M., E.Q., Y.O., L.Z., N.A., P.M., J.U. and M.W.). P.Y.C. and T.C. regularly updated the core group members by e-mail, and P.Y.C. led online meetings with updates on study progress and data collection and study outcomes with members of the scientific advisory board (N.B., I.B., Y.C., T.D., E.d.L., N.F., H.H., S.K., M.K., B.L., O.O., J.M.U.-R. and K.D.), youth advisory board (K.D., C.B., D.J., Y.O., E.Q. and L.Z.) and executive committee (N.A., J.U. and M.W.). P.Y.C. (the core group lead) and members of the scientific advisory board and executive committee were involved with conceptualization, study design and methodology. Youth advisers assisted with qualitative data analysis. P.Y.C., T.C. and A.M.-K. were also responsible for data curation and formal analysis; P.Y.C. and T.C. wrote the original draft, with contribution from G.P., M.S., T.W., H.H. and L.M. P.Y.C., T.C., A.M.-K., M.M., H.H. and E.d.L. reviewed and organized responses to reviewers. All co-authors reviewed responses to the reviewers. P.Y.C. led the manuscript revision with A.M.-K., M.M. and T.C. All co-authors had the opportunity to discuss the results, review full drafts of the manuscript and provide comments on the manuscript at all stages.

Corresponding author

Correspondence to Pamela Y. Collins .

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Extended data figures and tables

Extended data fig. 1 distribution of participants by nationality (n = 518) a,b,c ..

a Countries Participating: Argentina, Australia, Bangladesh, Cameroon, Canada, China, Colombia, Croatia, Czech Republic, Ecuador, Egypt, Ethiopia, France, Germany, Ghana, Haiti, Hong Kong, India, Iran, Italy, Kenya, Malawi, Mauritius, Mexico, Nepal, Netherlands, New Zealand, Nigeria, Norway, Pakistan, Papua New Guinea, Peru, Philippines, Poland, Rwanda, Samoa, Sierra Leone, Slovenia, South Africa, South Korea, Sweden, Switzerland, Taiwan, Tanzania, The Gambia, Tunisia, Turkey, Uganda, UK, USA, Venezuela, Zambia, Zimbabwe (53 total); b Two responses (“Asian” and “Indigenous and European”) do not list a nation but capture verbatim open-text responses; c Countries with one participant removed from graph and include: Argentina, Bangladesh, Cameroon, Croatia, Czech Republic, Ecuador, Egypt, Ethiopia, France, Haiti, Hong Kong, Indigenous and European, Mauritius, New Zealand, Norway, Papua New Guinea, Samoa, Slovenia, South Africa, South Korea, Switzerland, Taiwan, Tanzania, The Gambia, Tunisia, Turkey, Uganda, Venezuela.

Extended Data Fig. 2 Participant Nationality by Survey Round.

a SEA = South-East Asia, NA = North America*, AF = Africa, LSA = Latin & South America*, EU = Europe, WP = Western Pacific, EM = Eastern Mediterranean.

Extended Data Fig. 3 Distribution of Participants by WHO Region * and Survey Round.

a SEA = South-East Asia, NA = North America*, AF = Africa, LSA = Latin & South America*, EU = Europe, WP = Western Pacific, EM = Eastern Mediterranean; *We separated North America from Latin & South America for more transparent display of participant distribution.

Supplementary information

Supplementary information.

Supplementary Note which describes citiesRISE and lists the project team members of Making cities mental health-friendly for adolescents and young adults.

Reporting Summary

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Collins, P.Y., Sinha, M., Concepcion, T. et al. Making cities mental health friendly for adolescents and young adults. Nature (2024). https://doi.org/10.1038/s41586-023-07005-4

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