validity of research in nursing

Reliability and validity: Importance in Medical Research

Affiliations.

• 1 Al-Nafees Medical College,Isra University, Islamabad, Pakistan.
• 2 Fauji Foundation Hospital, Foundation University Medical College, Islamabad, Pakistan.
• PMID: 34974579
• DOI: 10.47391/JPMA.06-861

Reliability and validity are among the most important and fundamental domains in the assessment of any measuring methodology for data-collection in a good research. Validity is about what an instrument measures and how well it does so, whereas reliability concerns the truthfulness in the data obtained and the degree to which any measuring tool controls random error. The current narrative review was planned to discuss the importance of reliability and validity of data-collection or measurement techniques used in research. It describes and explores comprehensively the reliability and validity of research instruments and also discusses different forms of reliability and validity with concise examples. An attempt has been taken to give a brief literature review regarding the significance of reliability and validity in medical sciences.

Keywords: Validity, Reliability, Medical research, Methodology, Assessment, Research tools..

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Evidence-Based Practice for Nursing: Evaluating the Evidence

• What is Evidence-Based Practice?
• Asking the Clinical Question
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Evaluating Evidence: Questions to Ask When Reading a Research Article or Report

For guidance on the process of reading a research book or an article, look at Paul N. Edward's paper,  How to Read a Book  (2014) . When reading an article, report, or other summary of a research study, there are two principle questions to keep in mind:

1. Is this relevant to my patient or the problem?

• Once you begin reading an article, you may find that the study population isn't representative of the patient or problem you are treating or addressing. Research abstracts alone do not always make this apparent.
• You may also find that while a study population or problem matches that of your patient, the study did not focus on an aspect of the problem you are interested in. E.g. You may find that a study looks at oral administration of an antibiotic before a surgical procedure, but doesn't address the timing of the administration of the antibiotic.
• The question of relevance is primary when assessing an article--if the article or report is not relevant, then the validity of the article won't matter (Slawson & Shaughnessy, 1997).

2. Is the evidence in this study valid?

• Validity is the extent to which the methods and conclusions of a study accurately reflect or represent the truth. Validity in a research article or report has two parts: 1) Internal validity--i.e. do the results of the study mean what they are presented as meaning? e.g. were bias and/or confounding factors present? ; and 2) External validity--i.e. are the study results generalizable? e.g. can the results be applied outside of the study setting and population(s) ?
• Determining validity can be a complex and nuanced task, but there are a few criteria and questions that can be used to assist in determining research validity. The set of questions, as well as an overview of levels of evidence, are below.

For a checklist that can help you evaluate a research article or report, use our checklist for Critically Evaluating a Research Article

• How to Critically Evaluate a Research Article

How to Read a Paper--Assessing the Value of Medical Research

Evaluating the evidence from medical studies can be a complex process, involving an understanding of study methodologies, reliability and validity, as well as how these apply to specific study types. While this can seem daunting, in a series of articles by Trisha Greenhalgh from BMJ, the author introduces the methods of evaluating the evidence from medical studies, in language that is understandable even for non-experts. Although these articles date from 1997, the methods the author describes remain relevant. Use the links below to access the articles.

• How to read a paper: Getting your bearings (deciding what the paper is about) Not all published research is worth considering. This provides an outline of how to decide whether or not you should consider a research paper. more... less... Greenhalgh, T. (1997b). How to read a paper. Getting your bearings (deciding what the paper is about). BMJ (Clinical Research Ed.), 315(7102), 243–246.
• Assessing the methodological quality of published papers This article discusses how to assess the methodological validity of recent research, using five questions that should be addressed before applying recent research findings to your practice. more... less... Greenhalgh, T. (1997a). Assessing the methodological quality of published papers. BMJ (Clinical Research Ed.), 315(7103), 305–308.
• How to read a paper. Statistics for the non-statistician. I: Different types of data need different statistical tests This article and the next present the basics for assessing the statistical validity of medical research. The two articles are intended for readers who struggle with statistics more... less... Greenhalgh, T. (1997f). How to read a paper. Statistics for the non-statistician. I: Different types of data need different statistical tests. BMJ (Clinical Research Ed.), 315(7104), 364–366.
• How to read a paper: Statistics for the non-statistician II: "Significant" relations and their pitfalls The second article on evaluating the statistical validity of a research article. more... less... Greenhalgh, T. (1997). Education and debate. how to read a paper: Statistics for the non-statistician. II: "significant" relations and their pitfalls. BMJ: British Medical Journal (International Edition), 315(7105), 422-425. doi: 10.1136/bmj.315.7105.422
• How to read a paper. Papers that report drug trials more... less... Greenhalgh, T. (1997d). How to read a paper. Papers that report drug trials. BMJ (Clinical Research Ed.), 315(7106), 480–483.
• How to read a paper. Papers that report diagnostic or screening tests more... less... Greenhalgh, T. (1997c). How to read a paper. Papers that report diagnostic or screening tests. BMJ (Clinical Research Ed.), 315(7107), 540–543.
• How to read a paper. Papers that tell you what things cost (economic analyses) more... less... Greenhalgh, T. (1997e). How to read a paper. Papers that tell you what things cost (economic analyses). BMJ (Clinical Research Ed.), 315(7108), 596–599.
• Papers that summarise other papers (systematic reviews and meta-analyses) more... less... Greenhalgh, T. (1997i). Papers that summarise other papers (systematic reviews and meta-analyses). BMJ (Clinical Research Ed.), 315(7109), 672–675.
• How to read a paper: Papers that go beyond numbers (qualitative research) A set of questions that could be used to analyze the validity of qualitative research more... less... Greenhalgh, T., & Taylor, R. (1997). Papers that go beyond numbers (qualitative research). BMJ (Clinical Research Ed.), 315(7110), 740–743.

Levels of Evidence

In some journals, you will see a 'level of evidence' assigned to a research article. Levels of evidence are assigned to studies based on the methodological quality of their design, validity, and applicability to patient care. The combination of these attributes gives the level of evidence for a study.  Many systems for assigning levels of evidence exist.  A frequently used system in medicine is from the  Oxford Center for Evidence-Based Medicine .  In nursing, the system for assigning levels of evidence is often from Melnyk & Fineout-Overholt's 2011 book,  Evidence-based Practice in Nursing and Healthcare: A Guide to Best Practice .  The Levels of Evidence below are adapted from Melnyk & Fineout-Overholt's (2011) model.  

Uses of Levels of Evidence : Levels of evidence from one or more studies provide the "grade (or strength) of recommendation" for a particular treatment, test, or practice. Levels of evidence are reported for studies published in some medical and nursing journals. Levels of Evidence are most visible in Practice Guidelines, where the level of evidence is used to indicate how strong a recommendation for a particular practice is. This allows health care professionals to quickly ascertain the weight or importance of the recommendation in any given guideline. In some cases, levels of evidence in guidelines are accompanied by a Strength of Recommendation.

About Levels of Evidence and the Hierarchy of Evidence : While Levels of Evidence correlate roughly with the hierarchy of evidence (discussed elsewhere on this page), levels of evidence don't always match the categories from the Hierarchy of Evidence, reflecting the fact that study design alone doesn't guarantee good evidence. For example, the systematic review or meta-analysis of randomized controlled trials (RCTs) are at the top of the evidence pyramid and are typically assigned the highest level of evidence, due to the fact that the study design reduces the probability of bias  ( Melnyk , 2011),  whereas the weakest level of evidence is the  opinion from authorities and/or reports of expert committees.  However, a systematic review may report very weak evidence for a particular practice and therefore the level of evidence behind a recommendation may be lower than the position of the study type on the Pyramid/Hierarchy of Evidence.

About Levels of Evidence and Strength of Recommendation : The fact that a study is located lower on the Hierarchy of Evidence does not necessarily mean that the strength of recommendation made from that and other studies is low--if evidence is consistent across studies on a topic and/or very compelling, strong recommendations can be made from evidence found in studies with lower levels of evidence, and study types located at the bottom of the Hierarchy of Evidence. In other words, strong recommendations can be made from lower levels of evidence.

For example: a case series observed in 1961 in which two physicians who noted a high incidence (approximately 20%) of children born with birth defects to mothers taking thalidomide resulted in very strong recommendations against the prescription and eventually, manufacture and marketing of thalidomide. In other words, as a result of the case series, a strong recommendation was made from a study that was in one of the lowest positions on the hierarchy of evidence.

Hierarchy of Evidence for Quantitative Questions

The pyramid below represents the hierarchy of evidence, which illustrates the strength of study types; the higher the study type on the pyramid, the more likely it is that the research is valid. The pyramid is meant to assist researchers in prioritizing studies they have located to answer a clinical or practice question. 

For clinical questions, you should try to find articles with the highest quality of evidence. Systematic Reviews and Meta-Analyses are considered the highest quality of evidence for clinical decision-making and should be used above other study types, whenever available, provided the Systematic Review or Meta-Analysis is fairly recent. 

As you move up the pyramid, fewer studies are available, because the study designs become increasingly more expensive for researchers to perform. It is important to recognize that high levels of evidence may not exist for your clinical question, due to both costs of the research and the type of question you have.  If the highest levels of study design from the evidence pyramid are unavailable for your question, you'll need to move down the pyramid.

While the pyramid of evidence can be helpful, individual studies--no matter the study type--must be assessed to determine the validity.

Hierarchy of Evidence for Qualitative Studies

Qualitative studies are not included in the Hierarchy of Evidence above. Since qualitative studies provide valuable evidence about patients' experiences and values, qualitative studies are important--even critically necessary--for Evidence-Based Nursing. Just like quantitative studies, qualitative studies are not all created equal. The pyramid below  shows a hierarchy of evidence for qualitative studies.

Adapted from Daly et al. (2007)

Help with Research Terms & Study Types: Cut through the Jargon!

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Difference Between Reliability and Validity

Validity related to the data collection instrument (such as a questionnaire or interview).

Reliability: This usually has to do with HOW the data were collected. (Remember "data" is always plural!)

Research Rundowns

• Instrument, Validity, Reliability Instruments fall into two broad categories, researcher-completed and subject-completed, distinguished by those instruments that researchers administer versus those that are completed by participants. Researchers chose which type of instrument, or instruments, to use based on the research question.

What is "Reliability?"

Reliability has to do with the quality of measurement. In its everyday sense, reliability is the "consistency" or "repeatability" of your measures. Before we can define reliability precisely we have to lay the groundwork. First, you have to learn about the foundation of reliability, the true score theory of measurement .

Along with that, you need to understand the different types of measurement error because errors in measures play a key role in degrading reliability. With this foundation, you can consider the basic theory of reliability , including a precise definition of reliability. There you will find out that we cannot calculate reliability -- we can only estimate it.

Because of this, there a variety of different types of reliability that each have multiple ways to estimate reliability for that type. In the end, it's important to integrate the idea of reliability with the other major criteria for the quality of measurement -- validity -- and develop an understanding of the relationships between reliability and validity in measurement .

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• Open access
• Published: 22 April 2024

Developing and validating the nurse-patient relationship scale (NPRS) in China

• Yajie Feng 1   na1 ,
• Chaojie Liu 3 ,
• Siyi Tao 1 , 8 ,
• Chen Wang 1 , 6 ,
• Huanyu Zhang 1 ,
• Xinru Liu 1   na1 ,
• Zhaoyue Liu 1   na1 ,
• Wei Liu 1 ,
• Juan Zhao 1 , 5 ,
• Dandan Zou 1 , 7 ,
• Zhixin Liu 1 , 4 ,
• Junping Liu 1 ,
• Nan Wang 1 ,
• Qunhong Wu 1 ,
• Yanhua Hao 1 ,
• Weilan Xu 2 &
• Libo Liang 1  

BMC Nursing volume  23 , Article number:  255 ( 2024 ) Cite this article

176 Accesses

Metrics details

Poor nurse-patient relationship poses an obstacle to care delivery, jeopardizing patient experience and patient care outcomes. Measuring nurse-patient relationship is challenging given its multi-dimensional nature and a lack of well-established scales.

This study aimed to develop a multi-dimensional scale measuring nurse-patient relationship in China.

A preliminary scale was constructed based on the existing literature and Delphi consultations with 12 nursing experts. The face validity of the scale was tested through a survey of 45 clinical nurses. This was followed by a validation study on 620 clinical nurses. Cronbach’s α, content validity and known-group validity of the scale were assessed. The study sample was further divided into two for Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA), respectively, to assess the construct validity of the scale.

The Nurse-Patient Relationship Scale (NPRS) containing 23 items was developed and validated, measuring five dimensions: nursing behavior, nurse understanding and respect for patient, patient misunderstanding and mistrust in nurse, communication with patient, and interaction with patient. The Cronbach’s α of the NPRS ranged from 0.725 to 0.932, indicating high internal consistency. The CFA showed excellent fitness of data into the five-factor structure: χ 2 /df = 2.431, GFI = 0.933, TLI = 0.923, CFI = 0.939, IFI = 0.923, RMSEA = 0.070. Good content and construct validity are demonstrated through expert consensus and psychometric tests.

The NPRS is a valid tool measuring nurse-patient relationship in China.

Peer Review reports

Introduction

At present, nurse-patient disputes are common, and a large number of reports focus on the relationship and conflicts between nurses and patients. Despite efforts to alleviate the strained relationship between nurses and patients, it still persists [ 1 ]. Patients are usually considered as a passive subject [ 2 , 3 ]. Research points out that many patients, or most of them, are not able to engage in care for themselves through effective interactions with health workers [ 4 ]. Henderson [ 5 ] noted that professional domination over patient care causes depersonalization and, consequently, worsening of the relationship between the nurse and the patient [ 2 , 6 ].

A positive nurse-patient relationship is fundamental for effective and high-quality nursing care. The importance of defining and evaluating the connotation of the nurse-patient relationship has been well-established, with a variety of theories being proposed [ 7 , 8 , 9 ]. Some scholars define it as a kind of interpersonal relationship in the process of providing and receiving nursing services. Nurses and patients learn and encourage each other, naturally forming a relationship of helping and being helped [ 10 ]. Others see it as instrumental, primarily reflecting the help nurses provide to patients [ 11 ]. From the perspective of nurses, a positive nurse-patient relationship allows them to effectively plan, provide, and evaluate nursing services. For patients, the caring consciousness, wisdom, and interpersonal skills of nurses are essential for developing and maintaining a continuous nurse-patient relationship [ 12 ]. Clinical and interpersonal skills are the two equally important pillars of patient-centered nursing practice [ 13 ].

It is critical for nurses to form a positive attitude towards patients that involves respect, trust, and understanding to enable effectively communication and delivery of the help and guidance needed by the patients [ 14 ]. Empirical evidence suggests that the tension between nurses and patients is associated with a lack of respect and understanding of nursing care from patients. Some patients or the public may hold inherent prejudices toward the status and nature of nursing work, resulting in a lack of respect and understanding for nurses [ 15 ]. This can manifest in behaviors such as not treating nurses with respect or understanding their role. In some extreme cases, patients may resort to verbal and even physical violence against nurses, which can have a negative impact on the nurse-patient relationship. As a result, the nurses may be unable to provide high-quality nursing services [ 16 ].

A reliable tool measuring nurse-patient relationship can not only help to better understand the nursing care process, but also predict patient experience and care outcomes [ 7 , 8 , 9 ]. However, the existing validated tools measuring the nurse-patient relationship have several limitations. Firstly, there is a lack of comprehensiveness, with most focusing on specific selected aspects of the nurse-patient relationship, such as trust [ 17 , 18 ], social interaction [ 19 ], and care behavior [ 20 ]. Secondly, there exists ambiguity in the conceptualization of the elements measured by the existing tools: for example, “respect” can be regarded as an attribute of trust [ 21 ] or nursing behavior [ 20 , 22 ]. Thirdly, the existing tools have failed to consider the special circumstances of nursing work environments in China. The hierarchical and collectivist culture in China has significant implications for how nurses work with their patients and colleagues. Nurses often become an easy target for patient complaints although system problems are usually the underlying reasons [ 23 ]. Therefore, there is a need to develop a measurement tool that can capture the complex nature of nurse-patient relationship, especially under the context of the Chinese health system [ 24 ].

This study aimed to address the gap in the literature by developing and validating a scale that measures the nurse-patient relationship comprehensively from the perspective of nurses in China, guided by existing theories and considering the existing measurement tools.

The study followed the best practice in scale development [ 25 ], which involved four steps: item generation, content verification, scale refinement, and reliability and validity assessment (Fig.  1 ).

Four steps in scale development. (Note: EFA– Exploratory Factor Analysis; CFA– Confirmatory Factor Analysis; NPRS– Nurse-Patient Relationship Scale)

The study was conducted in Heilongjiang, a province with a socioeconomic development index at the lower end range in China. In 2019, Heilongjiang had 26 nurses per 10,000 population, compared with a national average of 32 [ 26 ].

Item generation

The concept of nurse-patient relationship was defined as a therapeutic relationship in line with Peplau’s interpersonal relationship theory. Nurses play a variety of roles in helping patients, ranging from a communicator to a caregiver [ 12 ]. At the core of the relationship is trust, communication, mutual understanding, and clinical care. Halldorsdottir (2008) likened the two extremes of nurse-patient relationship as “bridge” and “wall” [ 27 ]. “Bridge” symbolizes openness of communication and connectivity felt by patients in their relationship with nurses. It represents patient-centeredness and easy access to nursing services. By contrast, “wall” symbolizes a lack of communication and indifference of nurses to patient demands, as well as mistrust between the two parties [ 27 ]. The items generated in this study covered both “wall” and “bridge” aspects in relation to trust, communication, understanding, and clinical care.

The sources of items came from a cascading decomposition of the aforementioned theoretical assumptions, a review of the existing measurement tools, and descriptive adaptation to the local health system and clinical practices. A total of 12 sub-domains were mapped into the four core functions of nurse-patient relationship through the process, with advisory support from six external experts who had complementary knowledge and expertise to the research team (Table  1 ).

Content verification - Delphi consultations

The Delphi method is one of the most commonly used procedures to establish content validity of a scale [ 28 ]. In this study, eligible participants of the Delphi consultations were the experts with a background of nursing research, clinical nursing, or psychology. A minimal of ten years of work experience in the relevant areas was required. The participants were recruited through a stratified convenience sampling strategy. In total, 12 experts from eight provinces participated in the Delphi consultations, covering the eastern developed, the central developing, and the western under-developed regions in China. Half of them worked in academic institutions and half in the healthcare industry.

The participants were invited to respond to the consultation questionnaire by email in December 2019. They were asked to rate the relative importance of each sub-domain on a five-point Likert scale ranging from 1 (disagree) to 5 (agree), and the relevance of each item to its respective sub-domain on a five-point Likert scale ranging from 1 (not relevant) to 5 (essential). Suggestions about modification, removal, or addition of items, sub-domains, and domains were also encouraged. Participation in the consultations was voluntary and verbal informed consent was obtained from each participant.

Consensus of the expert ratings was indicated by the percentage of agreement. The items/sub-domains that had a higher than 80% expert agreement and an over 4 average score were retained [ 29 ]. Two rounds of consultations were conducted. The first round resulted in some changes in the subdomains and items, although the four core functions (domains) remained unchanged. In round two, feedback of the round one results was provided, which included the rating results and the corresponding changes made such as removal, addition, and modification of items, sub-domains, and domains. Participants were asked to reconsider their ratings if needed. The 12 experts completed both rounds of consultations.

We also calculated the item content validity index (I-CVI) and the scale content validity index (S-CVI)/average: I-CVI > 0.78 and (S-CVI)/average of 0.90 or higher were deemed acceptable [ 30 , 31 ].

Pilot testing

The NPRS endorsed by the experts was tested in a convenience sample of 45 nurses selected from the clinical units (mainly internal medicine, surgery, ICU, and stomatology) of a tertiary hospital in Harbin, capital of Heilongjiang province. Participants were asked to self-complete the paper questionnaire independently. Cronbach’s α coefficient of the scale reached 0.795. No further changes were made as a result of the pilot testing.

Reliability and validity assessment

Reliability and validity of the NPRS were assessed through a questionnaire survey of clinical nurses in a public tertiary hospital in Qiqihar city in Heilongjiang province. The hospital employed 1093 clinical nurses who had direct contacts with patients. From 29 to 31 December 2019, the nurses working in the clinical units were invited to participate in the survey. Participation in the survey was anonymous and voluntary. Return of the questionnaire was deemed informed consent. In total, 721 questionnaires were distributed and 708 (86.5%) were returned. After removal of the invalid returned questionnaires, 620 (86.0%) were included for data analysis, representing 56.7% of the entire nursing workforce in the participating hospital.

Ethical considerations

Ethics approval for the study protocol was granted by the research ethics committee of Harbin Medical University.

Data analysis

Data were analyzed using SPSS 21.0 and AMOS 24.0. A two-sided p  value of less than 0.05 was considered statistically significant. A pairwise strategy was adopted in managing missing values.

Each item of the NPRS was rated on a five-point Likert scale, ranging from 1 (Strongly disagree) to 5 (Strongly agree). The direction of item scores was aligned before a summed score was calculated for each domain and the entire scale, with a higher score indicating a more positive nurse-patient relationship.

Construct validity was tested through exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The study sample was randomly divided into two mutually independent sub-samples, with 330 participants for EFA and 290 participants for CFA, respectively. The appropriateness of factor analyses was assessed using the Kaiser-Meyer-Olkin (KMO) measure (KMO ≥ 0.50) and Bartlett’s test of sphericity ( p  < 0.05) [ 32 ]. The EFA extracted factors with an eigenvalue greater than 1 using principal component analysis (PCA) with maximal rotation of variance. This allowed us to identify and eliminate poorly-fitted items, including those with a low factor load (< 0.4) on all factors and those with a high load (≥ 0.4) across multiple factors [ 33 ]. The CFA then assessed the fitness of data into the adjusted scale resulting from the EFA. A good model fit was indicated by Chi-square/degree of freedom (χ 2 /df ratio ranging from 1 to 3), goodness-of-fit index (GFI > 0.9), root mean square error of approximation (RMSEA < 0.08), a root mean square residual (RMR < 0.08), a comparative fit index (CFI > 0.9), a normalized fit index (NFI > 0.9), and Incremental Fit Index (IFI > 0.9) [ 34 ]. Convergent validity was assessed by composite reliability (CR > 0.70) [ 35 ] and average variance extracted (AVE > 0.5) from CFA [ 36 ]. Discriminant validity was assessed by comparing AVE with the Pearson correlation coefficients between domains: A good discriminant validity is indicated if the square root of AVE of each construct is greater than its correlations with the rest of the constructs [ 37 , 38 ].

Reliability was assessed by Cronbach’s α for the entire NPRS and its domains using the entire sample. A greater than 0.7 Cronbach’s α coefficient indicates good internal consistency [ 39 ].

Known-group validity was tested through student t tests using the entire sample, with a hypothesis that nurse-patient relationship varies by the personal characteristics of the nurse [ 40 , 41 ].

Content validity

Characteristics of delphi participants.

About one third of the participants of the Delphi consultations came from Heilongjiang province and over 40% aged between 30 and 40 years. Half held a doctoral degree and had more than 20 years of work experience. Over 58% of participants held a senior professional title (Table  2 ).

Results of Delphi consultations

The first round of consultations resulted in an increase of items from 25 to 27: five new items were suggested while three were removed (Table  3 ). The three items that were suggested by some experts for removal all had low levels of expert agreement. Wording changes were also suggested by the experts for nine items to reduce ambiguity and improve clarity (Supplementary Table 1 ). The four core functions (domains) remained unchanged.

The first round of Delphi consultations already achieved an I-CVI of 0.83 (22/25) and an (S-CVI)/average of 0.98, exceeding the recommended value.

The second round of consultations led to language modification of two items. One item was removed because it failed to reach agreement among the experts in both rounds of consultations (Table  2 ). This resulted in a final version of the NPRS, containing 26 items, measuring nurse patient understanding and respect (8 items), nurse-patient trust (4 items), nurse-patient communication (8 items), and nurse’s help and guidance to patients (6 items). The second round of Delphi consultations already achieved an I-CVI of 0.83 (22/26) and an (S-CVI)/average of 0.99, exceeding the recommended value.

Construct validity

Characteristics of survey participants.

Of the 620 clinical nurses surveyed, 88.1% were female and 46.0% aged between 26 and 35 years. Most were married (53.2%), obtained a university degree (59.0%), and worked in internal medicine (55.6%). Almost half (49.0%) had over five years of work experience and 70.6% held an intermediate or senior professional title. The two sub-divided samples had slightly different characteristics of study participants (Table  4 ).

Structural adjustment of the scale

The KMO (0.903) and Bartlett test of sphericity ( p  < 0.001) indicated appropriateness of the subsample ( n  = 330) for EFA. The EFA extracted five factors: nursing behavior ; nurse understanding and respect for patient; patient misunderstanding and mistrust; communication with patient; and interaction with patient. The five factors explained 68.06% of the total variance. Three items (item N7, N9, N16) with low factor loadings or cross loadings were removed, resulting in a 23-item NPRS (Table  5 ). The complete NPRS scale is shown in supplementary Table S3 .

The KMO (0.902) and Bartlett test of sphericity ( p  < 0.001) indicated appropriateness of the subsample ( n  = 290) for CFA. Excellent fitness of data into the five-factor structure in line with the EFA was found: χ 2 /df = 2.431, GFI = 0.933, TLI = 0.923, CFI = 0.939, IFI = 0.923, and RMSEA = 0.070. The vast majority of items had a factor loading greater than 0.70 on its respective domain (Supplementary Table S2 ).

Convergent and discriminatory validity

Convergent validity of the scale was confirmed by the CFA ( n  = 290), as indicated by the greater than 0.7 CR and greater than 0.5 AVE (Table  6 ).

The five domains were moderately correlated. The square root of the AVE value of each domain generated from the CFA ( n  = 290) was much greater than its correlation coefficients with other domains (Table  6 ), indicating good discriminant validity between dimensions.

Cronbach’s α

High levels of internal consistency were found for the entire scale and its five domains, as indicated by the higher than 0.7 Cronbach’α coefficients (Table  7 ).

Known group validity

There were statistically significant differences in the NPRS scores by gender and working experience (Table  8 ). Male nurses had lower scores (indicating poorer relationship) in two domains: patient misunderstanding and mistrust in nurse, and communication with patients, compared to female nurses ( p  < 0.01). Longer work experience was associated with higher scores (indicating better relationship) in two domains: nurse understanding and respect for patients, and interaction with patients ( p  < 0.05). Patient complaint was associated with a lower score (indicating poorer relationship) in one domain (patient misunderstanding and mistrust in nurse) despite a lack of significance in the difference of overall NPRS scores.

Discussion and conclusions

The current research represents an attempt to provide a clear conceptualization and a reliable and valid scale measuring the comprehensive nurse-patient relationship in China. This research closely followed the best practice in scale development, involving a series studies covering the generation of dimensions and initial items, verification of the content, refinement of the scale, and reliability and validity testing of the scale. Previous studies have endeavored to assess the nurse-patient relationship through specific theories [ 18 , 46 , 47 ]. The nurse-patient relationship is indeed multifaceted. From a practical standpoint, no single theory can entirely encapsulate the nature of the nurse-patient relationship. The nurse-patient relationship scale developed in this current study offers a comprehensive tool by incorporating and refining dimensions and items derived from previous studies.

The results showed that the NPRS developed by our research has good reliability and validity. It supports a multi-dimensional construct, with Cronbach’s alpha of the scale and its five domains well exceeding the acceptable value of 0.7. Good content and construct validity are demonstrated through expert consensus and psychometric tests.

The NPRS has captured all of the essential elements of nurse-patient relationship as measured by the existing measurement tools, including trust [ 18 , 48 ], communication and interaction [ 46 , 49 , 50 , 51 ], and respect and humanistic care [ 47 ]. It covers both positive and negative behavioral reflections of the nurse-patient relationship, and puts nursing responsiveness, care process, and care outcomes at the core of the relationship. Mutual understanding, trust and respect provide the foundation for a positive nurse-patient relationship [ 27 ], which enables positive behaviors and interactions between the two to ensure good care outcomes.

The NPRS can help managers and policymakers to better respond to the call for patient-centered care. Increasing tensions in the relationship between nurses and patients due to various reasons have been observed worldwide [ 52 ], prompting calls for improving work and cultural environments. In this current study, we found that patient complaints are associated with poorer nurse-patient relationship, characterized by patient misunderstanding and distrust in nurses. Indeed, experiencing patient complaints reduces job satisfaction and the quality of working life of nurses [ 53 ]. Nurses facilitate care through frequent and direct contact with patients and their families in almost all healthcare settings, particularly in hospitals [ 54 ]. Patient demands and expectations have never been so high due to the rapid technological advancement and increased affordability of care [ 55 ]. What follows is the increase in the workload and the high pressure imposed on nurses [ 56 ]. Constant and chronic occupational stress produce burnout, a prominent characteristic of nursing work [ 57 ]. Study shows that the inverse relationship between physician burnout and patient safety affects nurse-patient relationship [ 58 ]. On the other hand, patients may take improved care outcomes for granted [ 59 ]. Therefore, it is important to use a tool, such as the NPRS, to help nurses and their managers to identify key domains in the nurse-patient relationship for improvement.

Our findings have some policy implications on the current health system reform in China. We found that the male nurses have worse relationship with patients than their female counterparts. This may reflect the structural inequality in gender division of work: Female nurses take most of the care tasks [ 60 ]. Female nurses may be more sensitive than their male counterparts, have stronger empathy, communication and caring characteristics, and pay more attention to emotional communication [ 61 ]. A study on the humanistic care of male nurses showed that male nurses expressed humanistic care differently from female nurses. Female nurses were more inclined to use their unique mother-like image to care for patients, while male nurses mostly used professional behaviors to care for patients [ 62 ]. There is a need to address the gender inequality and strengthen the communication competency of male nurses.

In the current study, we found that longer work experience is associated with a better nurse-patient relationship, in terms of nurse understanding and respect for patient and interaction with patient. Benner argues that rich life experience and increased situation awareness can help nurses to better manage nurse-patient relationship [ 63 ]. Empirical evidence shows that nursing students can obtain both professional and personal growth, such as a rise in confidence and self-esteem, through accumulated experience in interactions with patients [ 64 ]. However, professional and managerial support is equally, if not more, important to enable nurses to excel in managing nurse-patient relationship. As indicated in the findings of this current study, longer work experience does not appear to improve nurse behavior, patient misunderstanding and mistrust in nurse, and communication with patient.

The current study has some limitations. The study sample was drawn from one hospital. Future studies should expand participants to a more representative sample. It is also important to examine the tool from patient perspective. The NPRS was developed under the context of the Chinese health system. Cross-cultural adaptation is needed should it be used in different health system settings.

The 23-item NPRS is a valid tool measuring the comprehensive relationship between nurses and patients under the context of the Chinese health system. It measures five domains: nursing behavior, nurse understanding and respect for patient, patient misunderstanding and mistrust in nurse, communication with patient, and interaction with patient. The NPRS presents an opportunity for nurses and their managers to reflect and identify key domains in nurse-patient relationship for improvement. Healthcare practitioners and policymakers can utilize this tool to pinpoint crucial areas for enhancing the development of a trusting and productive nurse-patient relationship.

Data availability

The data sets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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This study was supported by the National Natural Science Foundation of China (71673073 and 71974049) for providing funding.

Author information

Yajie Feng, Xinru Liu and Zhaoyue Liu are joint first authors.

Authors and Affiliations

School of Health Administration, Harbin Medical University, Harbin, China

Yajie Feng, Siyi Tao, Chen Wang, Huanyu Zhang, Xinru Liu, Zhaoyue Liu, Wei Liu, Juan Zhao, Dandan Zou, Zhixin Liu, Junping Liu, Nan Wang, Lin Wu, Qunhong Wu, Yanhua Hao & Libo Liang

Qiqihar Medical College, Qiqihar, China

Department of Public Health, School of Psychology and Public Health, La Trobe University, 3086, Melbourne, VIC, Australia

Chaojie Liu

Department of Health Policy and Management, School of Public Health, Peking University, 100191, Beijing, China

Southwest Hospital, Third Military Medical University (Army Medical University, 400000, Chongqing, China

Xinqiao Hospital, Third Military Medical University (Army Medical University, 400037, Chongqing, China

Jin Shan Hospital of Fudan University, 201508, Shanghai, China

Anhui Medical University, No.1166, Wangjiang West Road, Shushan District, Hefei, Anhui, China

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F.Y.J. and T.S.Y. and W.C and L.W. and Z.H.Y and L.X.R and Z.J and Z.D.D and L.Z.X and L.J.P and W.N and W.L. and L.Z.Y. conceptualized the study. F.Y.J and L.X.R and T.S.Y and W.C. and L.L.B. contributed to reagents methodology. F.Y.J and T.S.Y and W.C and L.W and Z.H.Y and L.X.R and Z.J and Z.D.D. L.Z.X. and L.J.P. supervised data collection. F.Y.J and L.L.B and W.Q.H. and X.W.L. directed data analysis. F.Y.J and L.L.B and L.C.J and H.Y.H. and X.W.L. interpreted the findings. F.Y.J. drafted the manuscript. All authors reviewed the manuscript and approved the final version.

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Correspondence to Yanhua Hao , Weilan Xu or Libo Liang .

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This study was approved by the Ethical Review Board at the Harbin Medical University. The data collection occurred between October and December 2019. Abiding by the research ethical conduct, informed consent was obtained from all subjects who participated in this study. Research is carried out in accordance with the principles of the Declaration of Helsinki.

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Feng, Y., Liu, C., Tao, S. et al. Developing and validating the nurse-patient relationship scale (NPRS) in China. BMC Nurs 23 , 255 (2024). https://doi.org/10.1186/s12912-024-01941-w

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• Volume 17, Issue 4
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• Joanna Smith 1 ,
• Helen Noble 2
• 1 School of Human and Health Sciences, University of Huddersfield , Huddersfield , UK
• 2 School of Nursing and Midwifery, Queens's University Belfast , Belfast , UK
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The aim of this article is to outline types of ‘bias’ across research designs, and consider strategies to minimise bias. Evidence-based nursing, defined as the “process by which evidence, nursing theory, and clinical expertise are critically evaluated and considered, in conjunction with patient involvement, to provide the delivery of optimum nursing care,” 1 is central to the continued development of the nursing professional. Implementing evidence into practice requires nurses to critically evaluate research, in particular assessing the rigour in which methods were undertaken and factors that may have biased findings.

What is bias in relation to research and why is understanding bias important?

Although different study designs have specific methodological challenges and constraints, bias can occur at each stage of the research process ( table 1 ). In quantitative research, the validity and reliability are assessed using statistical tests that estimate the size of error in samples and calculating the significance of findings (typically p values or CIs). The tests and measures used to establish the validity and reliability of quantitative research cannot be applied to qualitative research. However, in the broadest context, these terms are applicable, with validity referring to the integrity and application of the methods and the precision in which the findings accurately reflect the data, and reliability referring to the consistency within the analytical processes. 4

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Types of research bias

How is bias minimised when undertaken research?

Bias exists in all study designs, and although researchers should attempt to minimise bias, outlining potential sources of bias enables greater critical evaluation of the research findings and conclusions. Researchers bring to each study their experiences, ideas, prejudices and personal philosophies, which if accounted for in advance of the study, enhance the transparency of possible research bias. Clearly articulating the rationale for and choosing an appropriate research design to meet the study aims can reduce common pitfalls in relation to bias. Ethics committees have an important role in considering whether the research design and methodological approaches are biased, and suitable to address the problem being explored. Feedback from peers, funding bodies and ethics committees is an essential part of designing research studies, and often provides valuable practical guidance in developing robust research.

In quantitative studies, selection bias is often reduced by the random selection of participants, and in the case of clinical trials randomisation of participants into comparison groups. However, not accounting for participants who withdraw from the study or are lost to follow-up can result in sample bias or change the characteristics of participants in comparison groups. 7 In qualitative research, purposeful sampling has advantages when compared with convenience sampling in that bias is reduced because the sample is constantly refined to meet the study aims. Premature closure of the selection of participants before analysis is complete can threaten the validity of a qualitative study. This can be overcome by continuing to recruit new participants into the study during data analysis until no new information emerges, known as data saturation. 8

In quantitative studies having a well-designed research protocol explicitly outlining data collection and analysis can assist in reducing bias. Feasibility studies are often undertaken to refine protocols and procedures. Bias can be reduced by maximising follow-up and where appropriate in randomised control trials analysis should be based on the intention-to-treat principle, a strategy that assesses clinical effectiveness because not everyone complies with treatment and the treatment people receive may be changed according to how they respond. Qualitative research has been criticised for lacking transparency in relation to the analytical processes employed. 4 Qualitative researchers must demonstrate rigour, associated with openness, relevance to practice and congruence of the methodological approach. Although other researchers may interpret the data differently, appreciating and understanding how the themes were developed is an essential part of demonstrating the robustness of the findings. Reducing bias can include respondent validation, constant comparisons across participant accounts, representing deviant cases and outliers, prolonged involvement or persistent observation of participants, independent analysis of the data by other researchers and triangulation. 4

In summary, minimising bias is a key consideration when designing and undertaking research. Researchers have an ethical duty to outline the limitations of studies and account for potential sources of bias. This will enable health professionals and policymakers to evaluate and scrutinise study findings, and consider these when applying findings to practice or policy.

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Relationship between learning styles and clinical competency in nursing students

• Seyed Kazem Mousavi 1 , 2 ,
• Ali Javadzadeh 3 ,
• Hanieh Hasankhani 3 &
• Zahra Alijani Parizad 3  

BMC Medical Education volume  24 , Article number:  469 ( 2024 ) Cite this article

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The acquisition of clinical competence is considered the ultimate goal of nursing education programs. This study explored the relationship between learning styles and clinical competency in undergraduate nursing students.

A descriptive-correlational study was conducted in 2023 with 276 nursing students from the second to sixth semesters at Abhar School of Nursing, Zanjan University of Medical Sciences, Iran. Data were collected using demographic questionnaires, Kolb’s learning styles, and Meretoja’s clinical competence assessments completed online by participants. Data were analyzed using SPSS version 16, employing descriptive statistics and inferential tests (independent T-test, ANOVA, Pearson correlation) at a significance level 0.05.

The predominant learning styles among nursing students were divergent (31.2%), and the least common was convergent (18.4%). The overall clinical competency score was 77.25 ± 12.65. Also, there was a significant relationship between learning styles and clinical competency, so the clinical competency of students with accommodative and converging learning styles was higher. ( P  < 0.05).

The results of this study showed the association between learning styles and clinical competence in nursing students. It is recommended that educational programs identify talented students and provide workshops tailored to strengthen various learning styles associated with enhanced clinical competence.

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Introduction

Clinical competency is a multifaceted and nuanced concept that has been extensively explored and examined from various perspectives in recent years [ 1 ]. Its significance is underscored by the World Health Organization (WHO), which has identified the assessment and enhancement of nurses’ competencies as fundamental principles to uphold the quality of care. WHO defines nurses as competent when they can fulfill their professional responsibilities at the appropriate level, grade, and standard [ 2 ]. Factors such as evolving healthcare systems, the imperative for safe and cost-effective services, heightened community awareness of health issues, escalating expectations for quality care, and the demand for skilled healthcare professionals have elevated the importance of clinical competence in nursing and related fields [ 3 ]. Clinical competency is considered the ultimate objective and benchmark of nursing education effectiveness [ 4 ]. Notably, clinical education constitutes a pivotal component of nursing training, with over half of nursing programs dedicated to practical training [ 5 ]. A nursing student’s ability to become a proficient nurse at the bedside hinges on acquiring essential skills during their academic journey and attaining requisite qualifications [ 6 ]. Scholars argue that continual efforts to enhance educational quality are essential for upholding nursing care standards and improving clinical competence [ 7 , 8 ]. Understanding how learners acquire knowledge is crucial for enhancing educational quality, with learning styles playing a pivotal role in this process [ 9 ].

Learning refers to the relatively enduring behavioral changes from experiences [ 10 ]. Learning styles, a concept widely embraced by educational theorists in recent decades, pertain to individuals’ distinct approaches to processing information and acquiring knowledge [ 11 ]. These styles encompass cognitive and psychosocial traits that are relatively stable indicators of learners’ engagement with and response to their learning environments [ 12 ]. Among the myriad theories on learning styles, Kolb’s learning theory is particularly influential [ 13 ]. According to Kolb, learning can be categorized into four primary modes: concrete experience, abstract conceptualization, reflective observation, and active experimentation, yielding four learning styles—converging, diverging, assimilating, and accommodating [ 14 ].

People with a converging learning style excel at problem-solving, decision-making, and practical application by engaging in abstract conceptualization and active experimentation [ 15 ]. Diverging learners thrive on experiencing and closely observing situations, possessing a unique ability to view scenarios from multiple perspectives and synthesize information into a cohesive whole [ 16 ]. The assimilating learning style is characterized by a preference for deep thinking and thorough examination, with individual’s adept at organizing information and employing abstract concepts to comprehend complex situations [ 13 ]. Accommodating learners learn best through hands-on experiences and activities, demonstrating proficiency in working with tangible objects and gaining new insights through practical engagement [ 10 ]. Learning styles are essential in nursing education because the primary mission of nursing education programs is to train nurses who have the necessary knowledge, attitude, and skills to maintain and improve the health of society members, and in other words, have sufficient competence in providing their job duties [ 17 ].

So far, separate studies have been conducted on nursing students’ learning styles and clinical competency [ 4 , 8 , 13 , 17 ]. However, the relationship between these two concepts has received less attention from researchers. The first step in ensuring students’ academic success is to determine their learning style [ 11 ]. Professors’ awareness of the student’s learning styles and the relationship between these styles and the level of clinical competency provide a favorable opportunity to identify the styles with higher clinical competency and encourage students to use them as much as possible. Considering this importance, the researchers decided to design and implement the present study to investigate the relationship between learning styles and clinical competency in nursing students.

Materials and methods

Study design and sampling.

This study was a descriptive-correlational study conducted in 2023, investigating the relationship between learning styles and the clinical competency of undergraduate nursing students. The research involved all second to sixth-semester undergraduate nursing students from the Abhar School of Nursing affiliated with Zanjan University of Medical Sciences, Iran. Sampling was carried out as a census, with 276 students selected to participate in the study. The inclusion criteria included willingness to participate in the study, full-time employment in nursing, no prior clinical work experience, and no reported psychiatric diseases or medication use. Incomplete questionnaire completion was set as an exclusion criterion.

Instruments

The data collection tools included demographic questionnaires, Kolb’s learning styles questionnaire, and a modified Meretoja nursing clinical competency questionnaire. The demographic questionnaire gathered age, gender, marital status, semester, Grade Point Average (GPA), and interest in nursing.

The data collection tools included demographic questionnaires, Kolb’s learning styles questionnaire, and a modified Meretoja nursing clinical competency questionnaire. The demographic questionnaire gathered information such as age, gender, marital status, semester, Grade Point Average (GPA), and interest in nursing.

Kolb’s III learning styles questionnaire comprised 12 questions with four options each, requiring the student to select the option most similar to them. Each option represented one of the four main learning methods: concrete experience (CE), reflective observation (RO), abstract conceptualization (AC), and active experimentation (AE). Scores for the four learning styles were obtained from the total questions across the sections. By subtracting scores, two dimensions (AC - CE) and (AE - RO) were derived, determining the student’s learning styles as converging, diverging, accommodating, or assimilating [ 18 ]. This questionnaire has been used in various studies over the past 30 years, demonstrating validity and reliability. In Iran, Ghahrani et al. used the internal consistency method to determine the reliability of the questionnaire. They obtained Cronbach’s alpha of 71% in concrete experience, 68% in reflective observation, 71% in abstract conceptualization, and 71% in active experimentation [ 19 ]. Also, In the present study, the reliability value of this questionnaire was determined using Cronbach’s alpha method of 0.94.

Meretoja’s revised nursing clinical competency questionnaire contained 47 items across 5 areas of clinical competency: assisting patients (7 skills), teaching and guidance (12 skills), diagnostic measures (8 skills), therapeutic measures (5 skills), and occupational responsibilities (15 skills). Skills were rated on a four-point Likert scale, assessing the degree of skill utilization [ 20 ]. This questionnaire was psychometrically evaluated in Iran by Bahreini et al., and its validity was qualitatively determined at the optimal level, and its reliability was determined between 70 and 85%. Also, in this study, the reliability value of this questionnaire was determined using Cronbach’s alpha method of 0.91 [ 21 ].

Data collection and statistical analysis

Following ethical approval and research permission, questionnaires, consent forms, and contact information for the researchers were provided to students online through the Porsline system ( www.porsline.ir ) for completion. Data analysis was performed using SPSS version 16 software, employing descriptive (frequency, percentage, mean, and standard deviation) and inferential (independent T-test, ANOVA, Pearson correlation) statistics at a significance level of 0.05.

Out of 276 participants, 10 students were excluded due to incomplete questionnaire responses, leaving 266 participants for analysis. The average age of students was 22.33, with 63.4% being female. Most participants were single (79.2%), and 46.2% had a GPA between 16 and 18. Also, 80.7% of students declared that they are interested in nursing. Then, the results of the questionnaires on learning styles and clinical competency were examined. Based on this, the findings showed that divergent (31.2%) and convergent (18.4%) styles were the study participants’ most and least-used learning styles, respectively. Also, the overall students’ clinical competence score was 77.25 ± 12.65 (Table  1 ).

The relationship between the participants’ learning styles and clinical competency was examined in the next step. Initially, the study data underwent a normality assessment. The Kolmogorov-Smirnov test results indicated that parametric statistical tests were applicable ( p  > 0.05). Subsequently, an ANOVA test was conducted to explore the relationship between learning styles and clinical competency, revealing a significant association between learning style and clinical competency with moderate effect size ( p  < 0.05) (Table  2 ).

The correlation between demographic variables, learning styles, and student clinical competency was investigated in the final phase of analyzing the findings. Parametric independent t-tests, Pearson’s correlation coefficient, and ANOVA were employed for this analysis. The results indicated that none of the learning styles exhibited a statistically significant relationship with the demographic characteristics of the participants ( p  > 0.05). However, a significant correlation was observed between participants’ demographic variables, such as age, academic semester, GPA, and interest in nursing, and their clinical competencies ( p  < 0.05) (Table  3 ).

This study explored the relationship between learning styles and clinical competency in undergraduate nursing students. The research initially focused on examining the variables and subsequently explored their interrelation. According to this, the most prevalent learning style among nursing students was divergent. This finding aligns with the outcomes of various domestic studies like Mehni et al. [ 22 ] and Shirazi et al. [ 16 ], as well as numerous international studies such as those by Campos et al. in Brazil and the United States [ 23 ], Nosheen in Pakistan [ 24 ], Madu et al. in Nigeria [ 25 ], and AbuAssi et al. in Saudi Arabia [ 26 ]. It should be said that the dominant abilities of people with divergent styles are concrete experience and reflective observation. They see the situation from multiple angles, emphasize brainstorming and generating ideas, have a strong imagination, are more sensitive to values, respect the feelings of others, and listen with an open mind and without bias [ 5 ]. Therefore, these people have high cultural interests and are more inclined towards humanities fields such as sociology, psychology, counseling, and nursing.

Upon reviewing studies in this field, it is concluded that findings often vary. They can be influenced by factors including individual student traits, educators’ teaching styles, learning environments, and tasks [ 12 ]. Also, this study noted no significant association between learning styles and participants’ demographic characteristics, consistent with similar research in the field [ 22 , 23 ]. In this regard, Dantas et al. emphasized that learning styles predominantly reflect individuals’ traits and are minimally impacted by demographic variables [ 12 ].

Furthermore, the clinical competency level of nursing students was reported to be at an average level, consistent with findings from studies conducted in Iran [ 6 , 27 , 28 ] and other countries [ 29 , 30 , 31 ]. Some studies, however, have yielded differing results compared to the present study. For instance, Ghafari et al. [ 1 ], Katebi et al. [ 32 ], and Fung et al. [ 33 ] found that nursing students participating in their studies exhibited a higher level of clinical competency. Notably, participants in all three mentioned studies were in their final year of study undergoing the arena course. Hence, the emphasis on passing diverse training units and gaining more clinical experiences could justify the high clinical competency score achieved. Also, In the present study, the relationship between academic semesters and students’ clinical competency was significant, which confirms the above argument. Conversely, certain studies have reported a lower level of clinical competency among nursing students. For example, Getie et al. found that only one-third of nursing students demonstrated acceptable clinical competency [ 34 ]. These discrepancies in findings can stem from the questionnaire and the data collection method used. Notably, Getie et al. evaluated students’ clinical competency through assessments by cooperating nurses rather than self-assessment. Additionally, adjusting the questionnaire averages to reflect higher clinical competence could impact the reported competency levels. Various factors, such as individual, environmental, organizational, and educational characteristics, influence the acquisition of clinical competency in nursing students [ 2 ]. Hence, diverse study outcomes exist in this field. Also, besides academic semesters, relationships were observed between age, GPA, interest in the field, and nursing students’ clinical competency. These results align with the findings of many studies in this area [ 27 , 28 , 29 , 32 , 33 ]. Older students are often in their final semesters, potentially showcasing higher clinical competency due to their exposure to clinical environments. Madjid et al. highlighted in their study that good grades obtained by learners in any field indicate their interest in the subject. This aspect holds particular significance in nursing—a complex and demanding profession where success hinges on a genuine interest and academic excellence [ 35 ].

Also, the study revealed a significant relationship between nursing students’ learning styles and clinical competency. According to this, students employing accommodating and converging learning styles reported heightened levels of clinical competency. In their study, Ebrahimi Fakhar et al. noted that medical students utilizing reflective observation and active experimentation learning methods exhibited enhanced clinical competency upon course completion. As these attributes align with accommodative and convergent learning style characteristics, the present study’s results are consistent with these findings [ 36 ]. Moon et al. found that nursing students with converging and accommodating styles reported increased competence in clinical practices [ 37 ]. Similarly, Lundell Rudberg et al. revealed that nursing students with these learning styles demonstrated greater professional responsibility, a key aspect of clinical competence. This correlation indirectly supports the present study’s outcomes [ 38 ]. Generally, it can be said that people with convergent and accommodating learning styles typically gravitate toward practical learning. They are inclined towards hands-on activities, deriving their learning mainly through experience and active participation [ 12 , 15 ]. Therefore, they are expected to engage more in clinical settings, fostering heightened clinical competency. Furthermore, the study’s latest findings indicated that students with an assimilating learning style exhibited lower levels of clinical competency, aligning with Moon et al.‘s study [ 37 ]. Similarly, Figueiredo et al. explored nurses’ learning styles based on Kolb’s theory in qualitative research, noting that nurses with assimilating learning styles are inclined towards abstract and subjective concepts over practical content and may have less enthusiasm for immersive clinical environments [ 39 ].

Limitations

One limitation of this study was the potential for inaccuracies in questionnaire completion due to the electronic data collection method and the extensive number of questions. To address this concern, participants were provided with researchers’ contact details for clarifications during data collection. Another limitation was the reliance on self-report tools and the omission of considering individuals’ personality traits in measuring the research variables, factors beyond researchers’ direct control.

The study’s findings underscore the relationship between learning styles and clinical competency in undergraduate nursing students. Therefore, considering the high importance of acquiring clinical competency in these students, it is recommended that educational administrators identify students prone to declining clinical competency based on their learning styles and organize workshops to enhance styles associated with superior clinical competency. Also, Given the complexity of learning styles and clinical competency as constructs, future studies may benefit from exploring additional theories and tools to delve deeper into these concepts, employing qualitative or mixed-method approaches for comprehensive analysis.

Data availability

Data is provided within the manuscript or supplementary information files.

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Seyed Kazem Mousavi

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Department of Nursing, School of Nursing and Midwifery, Guilan University of Medical Sciences, Rasht, Iran

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All the authors helped design the study. AJ and HH collected the data. SKM, and AJ analyzed and interpreted the data. All the authors helped write the manuscript and read and approved the final version.Funding.

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Permission to conduct the present study was obtained from the Research Department and Ethics Committee of Zanjan University of Medical Sciences (IR.ZUMS.REC.1402.095. available at: https://ethics.research.ac.ir/ ). All the study participants were informed about the objectives of the study, the confidentiality of the information, and the voluntary nature of their participation, and all students completed the informed consent form.

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Mousavi, S.K., Javadzadeh, A., Hasankhani, H. et al. Relationship between learning styles and clinical competency in nursing students. BMC Med Educ 24 , 469 (2024). https://doi.org/10.1186/s12909-024-05432-z

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Technology as a Tool for Improving Patient Safety

Introduction .

In the past several decades, technological advances have opened new possibilities for improving patient safety. Using technology to digitize healthcare processes has the potential to increase standardization and efficiency of clinical workflows and to reduce errors and cost across all healthcare settings. 1 However, if technological approaches are designed or implemented poorly, the burden on clinicians can increase. For example, overburdened clinicians can experience alert fatigue and fail to respond to notifications. This can lead to more medical errors. As a testament to the significance of this topic in recent years, several government agencies [(e.g. the Agency for Healthcare Research and Quality (AHRQ) and the Centers for Medicare and Medicaid services (CMS)] have developed resources to help healthcare organizations integrate technology, such as the Safety Assurance Factors for EHR Resilience (SAFER) guides developed by the Office of the National Coordinator for Health Information Technology (ONC). 2,3,4  However, there is some evidence that these resources have not been widely used.5 Recently, the Centers for Medicare & Medicaid Services (CMS) started requiring hospitals to use the SAFER guides as part of the FY 2022 Hospital Inpatient Prospective Payment Systems (IPPS), which should raise awareness and uptake of the guides. 6

During 2022, research into technological approaches was a major theme of articles on PSNet. Researchers reviewed all relevant articles on PSNet and consulted with Dr. A Jay Holmgren, PhD, and Dr. Susan McBride, PhD, subject matter experts in health IT and its role in patient safety. Key topics and themes are highlighted below.  

Clinical Decision Support  

The most prominent focus in the 2022 research on technology, based on the number of articles published on PSNet, was related to clinical decision support (CDS) tools. CDS provides clinicians, patients, and other individuals with relevant data (e.g. patient-specific information), purposefully filtered and delivered through a variety of formats and channels, to improve and enhance care. 7   

Computerized Patient Order Entry  

One of the main applications of CDS is in computerized patient order entry (CPOE), which is the process used by clinicians to enter and send treatment instructions via a computer application. 8 While the change from paper to electronic order entry itself can reduce errors (e.g., due to unclear handwriting or manual copy errors), research in 2022 showed that there is room for improvement in order entry systems, as well as some promising novel approaches. 

Two studies looked at the frequency of and reasons for medication errors in the absence of CDS and CPOE and demonstrated that there was a clear patient safety need. One study found that most medication errors occurred during the ordering or prescribing stage, and both this study and the other study found that the most common medication error was incorrect dose. Ongoing research, such as the AHRQ Medication Safety Measure Development project, aims to develop and validate measure specifications for wrong-patient, wrong-dose, wrong-medication, wrong-route, and wrong-frequency medication orders within EHR systems, in order to better understand and capture health IT safety events.9 Errors of this type could be avoided or at least reduced through the use of effective CPOE and CDS systems. However, even when CPOE and CDS are in place, errors can still occur and even be caused by the systems themselves. One study reviewed duplicate medication orders and found that 20% of duplicate orders resulted from technological issues, including alerts being overridden, alerts not firing, and automation issues (e.g., prefilled fields). A case study last year Illustrated one of the technological issues, in this case a manual keystroke error, that can lead to a safety event. A pharmacist mistakenly set the start date for a medication to the following year rather than the following day , which the CPOE system failed to flag. The authors recommended various alerts and coding changes in the system to prevent this particular error in the future.  

There were also studies in 2022 that showed successful outcomes of well-implemented CPOE systems. One in-depth pre-post, mixed-methods study showed that a fully implemented CPOE system significantly reduced specific serious and commonly occurring prescribing and procedural errors. The authors also presented evidence that it was cost-effective and detailed implementation lessons learned drawn from the qualitative data collected for the study. A specific CPOE function that demonstrated statistically significant improvement in 2022 was automatic deprescribing of medication orders and communication of the relevant information to pharmacies. Deprescribing is the planned and supervised process of dose reduction or stopping of a medication that is no longer beneficial or could be causing harm. That study showed an immediate and sustained 78% increase in successful discontinuations after implementation of the software. A second study on the same functionality determined that currently only one third to one half of medications are e-prescribed, and the study proposed that e-prescribing should be expanded to increase the impact of the deprescribing software. It should be noted, however, that the systems were not perfect and that a small percentage of medications were unintentionally cancelled. Finally, an algorithm to detect patients in need of follow-up after test results was developed and implemented in another study . The algorithm showed some process improvements, but outcome measures were not reported. 

Usability  

Usability of CDS systems was a large focus of research in 2022. Poorly designed systems that do not fit into existing workflows lead to frustrated users and increase the potential for errors. For example, if users are required to enter data in multiple places or prompted to enter data that are not available to them, they could find ways to work around the system or even cease to use it, increasing the potential for patient safety errors. The documentation burden is already very high on U.S. clinicians, 10 so it is important that novel technological approaches do not add to this burden but, if possible, alleviate it by offering a high level of usability and interoperability.  

One study used human-factored design in creating a CDS to diagnose pulmonary embolism in the Emergency Department and then surveyed clinician users about their experiences using the tool. Despite respondents giving the tool high usability ratings and reporting that the CDS was valuable, actual use of the tool was low. Based on the feedback from users, the authors proposed some changes to increase uptake, but both users and authors mentioned the challenges that arise when trying to change the existing workflow of clinicians without increasing their burden. Another study gathered qualitative feedback from clinicians on a theoretical CDS system for diagnosing neurological issues in the Emergency Department. In this study too, many clinicians saw the potential value in the CDS tool but had concerns about workflow integration and whether it would impact their ability to make clinical decisions. Finally, one study developed a dashboard to display various risk factors for multiple hospital-acquired infections and gathered feedback from users. The users generally found the dashboard useful and easy to learn, and they also provided valuable feedback on color scales, location, and types of data displayed. All of these studies show that attention to end user needs and preferences is necessary for successful implementation of CDS.  However, the recent market consolidation in Electronic Health Record vendors may have an impact on the amount of user feedback gathered and integrated into CDS systems. Larger vendors may have more resources to devote to improving the usability and design of CDS, or their near monopolies in the market may not provide an incentive to innovate further. 11 More research is needed as this trend continues.  

Alerts and Alarms 

Alerts and alarms are an important part of most CDS systems, as they can prompt clinicians with important and timely information during the treatment process. However, these alerts and alarms must be accurate and useful to elicit an appropriate response. The tradeoff between increased safety due to alerts and clinician alert fatigue is an important balance to strike. 12

Many studies in 2022 looked at clinician responses to medication-related alerts, including override and modification rates. Several of the studies found a high alert override rate but questioned the validity of using override rates alone as a marker of CDS effectiveness and usability. For example, one study looked at drug allergy alerts and found that although 44.8% of alerts were overridden, only 9.3% of those were inappropriately overridden, and very few overrides led to an adverse allergic reaction. A study on “do not give” alerts found that clinicians modified their orders to comply with alert recommendations after 78% of alerts but only cancelled orders after 26% of alerts. A scoping review looked at drug-drug interaction alerts and found similar results, including high override rates and the need for more data on why alerts are overridden. These findings are supported by another study that found that the underlying drug value sets triggering drug-drug interaction alerts are often inconsistent, leading to many inappropriate alerts that are then appropriately overridden by clinicians. These studies suggest that while a certain number of overrides should be expected, the underlying criteria for alert systems should be designed and regularly reviewed with specificity and sensitivity in mind. This will increase the frequency of appropriate alerts that foster indicated clinical action and reduce alert fatigue. 

There also seems to be variability in the effectiveness of alert systems across sites. One study looked at an alert to add an item to the problem list if a clinician placed an order for a medication that was not indicated based on the patient’s chart. The study found about 90% accuracy in alerts across two sites but a wide difference in the frequency of appropriate action between the sites (83% and 47%). This suggests that contextual factors at each site, such as culture and organizational processes, may impact success as much as the technology itself.  

A different study looked at the psychology of dismissing alerts using log data and found that dismissing alerts becomes habitual and that the habit is self-reinforcing over time. Furthermore, nearly three quarters of alerts were dismissed within 3 seconds. This indicates how challenging it can be to change or disrupt alert habits once they are formed. 

Artificial Intelligence and Machine Learning  

In recent years, one of the largest areas of burgeoning technology in healthcare has been artificial intelligence (AI) and machine learning. AI and machine learning use algorithms to absorb large amounts of historical and real-time data and then predict outcomes and recommend treatment options as new data are entered by clinicians. Research in 2022 showed that these techniques are starting to be integrated into EHR and CDS systems, but challenges remain. A full discussion of this topic is beyond the scope of this review. Here we limit the discussion to several patient-safety-focused resources posted on PSNet in 2022.  

One of the promising aspects of AI is its ability to improve CDS processes and clinician workflow overall. For example, one study last year looked at using machine learning to improve and filter CDS alerts. They found that the software could reduce alert volume by 54% while maintaining high precision. Reducing alert volume has the potential to alleviate alert fatigue and habitual overriding. Another topic explored in a scoping review was the use of AI to reduce adverse drug events. While only a few studies reviewed implementation in a clinical setting (most evaluated algorithm technical performance), several promising uses were found for AI systems that predict risk of an adverse drug event, which would facilitate early detection and mitigate negative effects.  

Despite enthusiasm for and promising applications of AI, implementation is slow. One of the challenges facing implementation is the variable quality of the systems. For example, a commonly used sepsis detection model was recently found to have very low sensitivity. 13 Algorithms also drift over time as new data are integrated, and this can affect performance, particularly during and after large disturbances like the COVID-19 pandemic. 14 There is also emerging research about the impact of AI algorithms on racial and ethnic biases in healthcare; at the time of publication of this essay, an AHRQ EPC was conducting a review of evidence on the topic. 15  These examples highlight the fact that AI is not a “set it and forget it” application; it requires monitoring and customization from a dedicated resource to ensure that the algorithms perform well over time. A related challenge is the lack of a strong business case for using high-quality AI. Because of this, many health systems choose to use out-of-the-box AI algorithms, which may be of poor quality overall (or are unsuited to particular settings) and may also be “black box” algorithms (i.e., not customizable by the health system because the vendor will not allow access to the underlying code). 16 The variable quality and the lack of transparency may cause mistrust by clinicians and overall aversion to AI interventions.  

In an attempt to address these concerns, one article in 2022 detailed best practices for AI implementation in health systems, focusing on the business case. Best practices include using AI to address a priority problem for the health system rather than treating it as an end itself. Additionally, testing the AI using the health system’s patients and data to demonstrate applicability and accuracy for that setting, confirming that the AI can provide a return on investment, and ensuring that the AI can be implemented easily and efficiently are also important. Another white paper described a human-factors and ergonomics framework for developing AI in order to improve the implementation within healthcare systems, teams, and workflows. The federal government and international organizations have also published AI guidelines, focusing on increasing trustworthiness (National Artificial Intelligence Initiative) 17 and ensuring ethical governance (World Health Organization). 18   

Conclusion and Next Steps 

As highlighted in this review, the scope and complexity of technology and its application in healthcare can be intimidating for healthcare systems to approach and implement. Researchers last year thus created a framework that health systems can use to assess their digital maturity and guide their plans for further integration.  

The field would benefit from more research in several areas in upcoming years. First and foremost, high-quality prospective outcome studies are needed to validate the effectiveness of the new technologies. Second, more work is needed on system usability, how the systems are integrated into workflows, and how they affect the documentation burden placed on clinicians. For CDS specifically, more focus is needed on patient-centered CDS (PC CDS), which supports patient-centered care by helping clinicians and patients make the best decisions given each individual’s circumstances and preferences. 19 AHRQ is already leading efforts in this field with their CDS Innovation Collaborative project. 20 Finally, as it becomes more common to incorporate EHR scribes to ease the documentation burden, research on their impact on patient safety will be needed, especially in relation to new technological approaches. For example, when a scribe encounters a CDS alert, do they alert the clinician in all cases? 

In addition to the approaches mentioned in this article, other emerging technologies in early stages of development hold theoretical promise for improving patient safety. One prominent example is “computer vision,” which uses cameras and AI to gather and process data on what physically happens in healthcare settings beyond what is captured in EHR data, 21 including being able to detect immediately that a patient fell in their room. 22  

As technology continues to expand and improve, researchers, clinicians, and health systems must be mindful of potential stumbling blocks that could impede progress and threaten patient safety. However, technology presents a wide array of opportunities to make healthcare more integrated, efficient, and safe.  

• Cohen CC, Powell K, Dick AW, et al. The Association Between Nursing Home Information Technology Maturity and Urinary Tract Infection Among Long-Term Residents . J Appl Gerontol . 2022;41(7):1695-1701. doi: 10.1177/07334648221082024. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9232878/
• https://www.healthit.gov/topic/safety/safer-guides
• https://cds.ahrq.gov/cdsconnect/repository
• https://www.cms.gov/about-cms/obrhi
• McBride S, Makar E, Ross A, et al. Determining awareness of the SAFER guides among nurse informaticists. J Inform Nurs. 2021;6(4). https://library.ania.org/ania/articles/713/view
• Sittig DF, Sengstack P, Singh H. Guidelines for US hospitals and clinicians on assessment of electronic health record safety using SAFER guides. J ama . 2022;327:719-720.
• https://library.ahima.org/doc?oid=300027#.Y-6RhXbMKHt
• https://www.healthit.gov/faq/what-computerized-provider-order-entry#:~:text=Computerized%20provider%20order%20entry%20(CPOE,paper%2C%20fax%2C%20or%20telephone
• https://digital.ahrq.gov/2018-year-review/research-spotlights/leveragin…
• Holmgren AJ, Downing NL, Bates DW, et al. Assessment of electronic health record use between US and non-US health systems. JAMA Intern Med. 2021;181:251-259. https://doi.org/10.1001/jamainternmed.2020.7071
• Holmgren AJ, Apathy NC. Trends in US hospital electronic health record vendor market concentration, 2012–2021. J Gen Intern Med. 2022. https://link.springer.com/article/10.1007/s11606-022-07917-3#citeas
• Co Z, Holmgren AJ, Classen DC, et al. The tradeoffs between safety and alert fatigue: data from a national evaluation of hospital medication-related clinical decision support. J Am Med Inform Assoc. 2020;27:1252-1258. https://pubmed.ncbi.nlm.nih.gov/32620948/
• Wong A, Otles E, Donnelly JP, et al. External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Intern Med. 2021;181:1065-1070. https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2781307
• Parikh RB, Zhang Y, Kolla L, et al. Performance drift in a mortality prediction algorithm among patients with cancer during the SARS-CoV-2 pandemic. J Am Med Inform Assoc. 2022;30:348-354. https://academic.oup.com/jamia/advance-article/doi/10.1093/jamia/ocac221/6835770?login=false
• https://effectivehealthcare.ahrq.gov/products/racial-disparities-health…
• https://www.statnews.com/2022/05/24/market-failure-preventing-efficient-diffusion-health-care-ai-software/
• https://www.ai.gov/strategic-pillars/advancing-trustworthy-ai/
• Ethics and governance of artificial intelligence for health (WHO guidance). Geneva: World Health Organization; 2021. https://www.who.int/publications/i/item/9789240029200
• Dullabh P, Sandberg SF, Heaney-Huls K, et al. Challenges and opportunities for advancing patient-centered clinical decision support: findings from a horizon scan. J Am Med Inform Assoc. 2022: 29(7):1233-1243. doi: 10.1093/jamia/ocac059. PMID: 35534996; PMCID: PMC9196686.
• https://cds.ahrq.gov/cdsic
• Yeung S, Downing NL, Fei-Fei L, et al. Bedside computer vision: moving artificial intelligence from driver assistance to patient safety. N Engl J Med. 2018;387:1271-1273. https://www.nejm.org/doi/10.1056/NEJMp1716891
• Espinosa R, Ponce H, Gutiérrez S, et al. A vision-based approach for fall detection using multiple cameras and convolutional neural networks: a case study using the UP-Fall detection dataset. Comput Biol Med. 2019;115:103520. https://doi.org/10.1016/j.compbiomed.2019.103520

This project was funded under contract number 75Q80119C00004 from the Agency for Healthcare Research and Quality (AHRQ), U.S. Department of Health and Human Services. The authors are solely responsible for this report’s contents, findings, and conclusions, which do not necessarily represent the views of AHRQ. Readers should not interpret any statement in this report as an official position of AHRQ or of the U.S. Department of Health and Human Services. None of the authors has any affiliation or financial involvement that conflicts with the material presented in this report. View AHRQ Disclaimers

Perspective

Perspectives on Safety

Annual Perspective

Patient Safety Innovations

Suicide Prevention in an Emergency Department Population: ED-SAFE

WebM&M Cases

The Retrievals. August 9, 2023

Agent of change. August 1, 2018

Amid lack of accountability for bias in maternity care, a California family seeks justice. August 16, 2023

Mirror, Mirror on the Wall: An Update on the Quality of American Health Care Through the Patient's Lens. April 12, 2006

Improving patient safety by shifting power from health professionals to patients. October 25, 2023

Patient Safety Primers

Discharge Planning and Transitions of Care

Medicines-related harm in the elderly post-hospital discharge. March 27, 2019

Emergency department crowding: the canary in the health care system. November 3, 2021

Advancing Patient Safety: Reviews From the Agency for Healthcare Research and Quality's Making Healthcare Safer III Report. September 2, 2020

Exploring Alternatives To Malpractice Litigation. January 15, 2014

Making Healthcare Safer III. March 18, 2020

Special Section: Patient Safety. May 24, 2006

The Science of Simulation in Healthcare: Defining and Developing Clinical Expertise. November 19, 2008

Compendium of Strategies to Prevent HAIs in Acute Care Hospitals 2014. September 1, 2014

Quality, Safety, and Noninterpretive Skills. November 11, 2015

Patient Safety. November 21, 2018

Ambulatory Safety Nets to Reduce Missed and Delayed Diagnoses of Cancer

Remote response team and customized alert settings help improve management of sepsis.

Using sociotechnical theory to understand medication safety work in primary care and prescribers' use of clinical decision support: a qualitative study. May 24, 2023

Human factors and safety analysis methods used in the design and redesign of electronic medication management systems: a systematic review. May 17, 2023

Journal Article

Reducing hospital harm: establishing a command centre to foster situational awareness.

The potential for leveraging machine learning to filter medication alerts. May 4, 2022

Improving the specificity of drug-drug interaction alerts: can it be done? April 6, 2022

A qualitative study of prescribing errors among multi-professional prescribers within an e-prescribing system. December 23, 2020

The tradeoffs between safety and alert fatigue: data from a national evaluation of hospital medication-related clinical decision support. July 29, 2020

Assessment of health information technology-related outpatient diagnostic delays in the US Veterans Affairs health care system: a qualitative study of aggregated root cause analysis data. July 22, 2020

Reducing drug prescription errors and adverse drug events by application of a probabilistic, machine-learning based clinical decision support system in an inpatient setting. August 21, 2019

Improving medication-related clinical decision support. March 7, 2018

The frequency of inappropriate nonformulary medication alert overrides in the inpatient setting. April 6, 2016

The effect of provider characteristics on the responses to medication-related decision support alerts. July 15, 2015

Best practices: an electronic drug alert program to improve safety in an accountable care environment. July 1, 2015

Impact of computerized physician order entry alerts on prescribing in older patients. March 25, 2015

Differences of reasons for alert overrides on contraindicated co-prescriptions by admitting department. December 17, 2014

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