who risk assessment methodology

Risk Assessment Toolkit

The negative impacts of any emergencies or disasters highlight the importance of the all-hazards risk management approach in all emergency preparedness and response actions, which builds on multi-sectorial and whole-of-society approach to ensure inclusive and non-discriminatory process of developing capacities in the communities.

Whilst all communities and countries are at risk of being exposed to all type of disasters, disaster risks vary between different countries and communities [1]. Therefore, a risk management approach emphasises on assessing risks to guide timely and effective actions and to provide evidence to strategies and policies for better prevention, preparedness, response and recovery [2].

Disaster risks in communities are directly influenced by the communities’ exposure to hazards, their vulnerabilities to those hazards, and their risk management capacity in all phases of disasters. Therefore, countries and communities can most effectively minimise the adverse impacts of emergencies with risk-specific capacities through preventing or mitigating impact of hazards, reducing exposure to those hazards, minimising vulnerabilities, and/or strengthening their capacities [3].

WHO developed   Strategic Tool for Assessing Risks (STAR)   in 2021, which  is a comprehensive, easy-to-use toolkit and approach to assess risks to guide actions, inform planning and provide evidence to strategies and policies for better prevention, preparedness, response and recovery, which are critical to whole of society actions for emergencies and disasters.

Therefore, in order to develope, implement and scale up Health EDRM related policies and programmes to increase readiness for likely risks, it is crucial to estimate disaster risks and their underlying risk factors through assessing:

• Severity of hazards to which populations are exposed,
• Vulnerability of of locations, population groups in communitites,
• Existing local capacities to respond to emergencies.

1. Identify Hazards

Hazards is a  process, phenomenon or human activity that may cause loss of life, injury or other health impacts, property damage, social and economic disruption or environmental degradation [3]. 

The UNDRR/ISC Sendai Hazard Definition and Classification Review Technical Report  (2020) includes a common set of 302 hazard information profiles to governments and stakeholders to inform their strategies and actions on risk reductions and management [5].

2. Identify Vulnerable Population

As explained in the WHO Health EDRM Knowledge Hub: Vulnerability and Vulnerable Populations , v ulnerability refers to the characteristics and circumstances of an individual, community, systems or assets that can influence their susceptability to the impact of a hazard and is dependent on various risk factors [3], including:

• Population demographic : e.g. gender, age, chronic conditions, immunity
• Social factors : e.g. literacy, unemployment, poverty, income status, poor social network
• Environmental factors : e.g. unsafe drinking-water, sanitation, food insecurity, unplanned urbanisation, climate change,  proximity of mosquito vector breeding sites, and conflict
• Political factors : e.g. luck of disaster risk management policies and programmes

Vulnerable populations may have high disaster risk due to the driving factors, for exampl

- In areas affected by climate and environmental threats

- Where essential services are inadequate, such as water and sanitation

- breakdown of family/community structures and network

There are multiple approaches to access vulnerability of populations. Data on disease mortality, morbidity, health risk factors, hazard exposure and coping capacity are analysed to identify most-at-risk populations, disadvantage or marginalised groups examine social-economic status, structural and cultural identity, and geographical condition.

3. Assess local disaster response capacity

Capacity refers to the combination of all the strengths, attributes and resources available within an organization, community or society to manage and reduce disaster risks and strengthen resilience.

Coping capacity  is the ability of people, organisations and systems, using available skills and resources, to manage adverse conditions, risk or disasters. This requires continuing awareness, resources and good management, both in normal times as well as during disasters or adverse conditions. Coping capacities contribute to the reduction of disaster risks.

highlight the importance of understanding that there is much capacity within communities and they are often the first responders.

• Hospital Safety Index : guide for evaluators, 2nd ed: Ho spital Safety Index is a tool, widely used by health authorities in all contexts across the world . This Guide for evaluators for the Hospital Safety Index provides a step-by-step explanation of how to use the Safe Hospitals Checklist, and how the evaluation can be used to obtain a rating of the structural and nonstructural safety, and the emergency and disaster management capacity, of the hospital. The results of the evaluation enable hospital’s own safety index to be calculated.
• WHO Checklists for Assess vulnerabilities in Health Care Facilities in the Context of Climate Change. This guide provides specific checklists to assess risks, vulnerabilities and impacts at the level of health care facilities. https://www.who.int/publications/i/item/9789240022904

[1] Saulnier DD, Dixit AM, Nunes AR, Murray V.(2022). Chapter 3.2 Disaster risk factors – hazards, exposure and vulnerability. In: WHO guidance on research methods for health emergency and disaster risk management, revised 2022. World Health Organization. pp. 151-163. https://apps.who.int/iris/handle/10665/363502 (Accessed 15 June 2023).

[2]  WHO (2021). Strategic Toolkit for Assessing Risks: A comprehensive toolkit for all-hazards health emergency risk assessment.  https://www.who.int/publications/i/item/9789240036086

[3] World Health Organization. (‎2019)‎. Health emergency and disaster risk management framework. World Health Organization.  https://apps.who.int/iris/handle/10665/326106 . License: CC BY-NC-SA 3.0 IGO

[4] United Nations Office for Disaster Risk Reduction. Terminology: Vulnerability. Available from:  https://www.undrr.org/terminology/vulnerability (Accessed 15 June 2023).

[5] United Nations Office for Disaster Risk Reduction & International Science Council. ( 2020).  UNDRR Hazard Definition and Classification Review Technical Report.  https://www.undrr.org/publication/hazard-definition-and-classification-review-technical-report

[6] United Nations Office for Disaster Risk Reduction. Sendai Framework terminology on Disaster Risk Reduction. https://www.undrr.org/terminology/hazard&nbsp ;(Accessed 15 June 2023).

• Text of the Convention
• How it works
• Frequently Asked Questions
• Proposed amendments
• Annex III Chemicals
• Decision Guidance Documents
• Harmonized System Codes
• Recommended for listing
• Candidate Chemicals
• Not yet recommended for listing
• Intersessional Work on Listing
• Overview and Mandate
• Rules of Procedure
• Reports and Decisions
• Call for Information
• CRC Recommendations
• Workshops and Webinars
• Compliance Committee
• Special Voluntary Trust Fund RC
• Communications
• Declaration of Conflicts of Interest
• Becoming a Party
• Admission of Observers
• Contacts Management
• Export Notifications
• Import Responses
• Notifications of Final Regulatory Actions
• Official Contact Points
• PIC Procedure
• Severely Hazardous Pesticide Formulations
• Programme of Work
• Intersessional work COP.11 to COP.12
• Impacts of listing chemicals
• PIC Circular
• Resource Kit
• E-Learning Tool
• FRA Evaluation Toolkit
• FAO Pesticide Registration Toolkit
• Needs Assessment
• Regional Offices
• Publications
• Survey on definitions of term pesticides
• Alternatives to hazardous pesticides
• Activities focused on alternatives
• Pesticides information database
• Environmental incidents
• Child labour
• Gender issues
• Legal matters
• Public Awareness
• Parties and Signatories
• Annex VII to the Rotterdam Convention
• Country Contacts
• National Legislation
• Country Profiles
• PIC Regions
• Secretariat Staff
• Business and Industry
• Academia and Research

Ressource Kit

Ressource kit sections, evaluating the risks of hazardous chemicals.

This section lists selected sources of information related to the evaluation of chemicals, including methodologies for evaluating the risks of chemicals. This information may be of use to countries in developing their own capacities to evaluate the risks associated with the use of hazardous chemicals and/or to better understand the processes followed at the international level in preparing evaluations of individual chemicals or groups of chemicals.

a) The WHO Recommended Classification of Pesticides by Hazard (2004)

This document sets out a classification system which distinguishes between the more and the less hazardous forms of selected pesticides based on acute risk to human health (i.e.the risk of single or multiple exposures over a relatively short period of time). It takes into consideration the toxicity of the technical compound and its common formulations. The main section of the classification consists of five tables which separate technical grade active ingredients into the three classes listed below. A further table lists active ingredients unlikely to present acute hazard in normal use:

• Extremely hazardous (Class Ia)
• Highly hazardous (Class Ib)
• Moderately hazardous (Class II)
• Slightly hazardous (Class III)

b) Listing of WHO/IPCS publications and projects on risk assessment methodology

The World Health Organization's International Programme on Chemical Safety (IPCS) develops information and guidance on risk assessment methodologies. These aim to promote the development, harmonization and use of generally acceptable, scientifically sound methodologies for the evaluation of risks to human health and the environment from exposure to chemicals. The results of such work enhance mutual acceptance of risk assessment products. The publications are available at: www.who.int/ipcs/publications/ehc/methodology_alphabetical/en/index.html and on the INCHEM database at: www.inchem.org/ A selection of these guidance documents (listed below) may be downloaded here:

c) OECD Guidelines for the Testing of Chemicals

The OECD guidelines are a collection of internationally agreed test methods used by government, industry and independent laboratories to determine the safety of chemicals and chemical preparations, including pesticides and industrial chemicals. They cover tests for the physico-chemical properties of chemicals, human health effects, environmental effects, and degradation and accumulation in the environment. These are the guidelines generally employed in developing scientific data concerning effects on human health and the environment submitted to regulatory authorities in support of the regulation of industrial chemicals or pesticides. http://titania.sourceoecd.org/vl=4637754/cl=43/nw=1/rpsv/periodical/p15_about.htm?jnlissn=1607310x

A Comprehensive Guide to Risk Assessment Methodology

August 16, 2023

Risk assessment methodology , an aspect of any risk management process , encompasses various approaches to determining the potential impact of identified risks.

This comprehensive guide aims to elucidate the different methodologies, spanning from quantitative, qualitative, semi-quantitative, asset-based, and vulnerability-based to threat-based.

Each methodology, distinct in its approach, bears its own strengths and limitations, thus necessitating careful selection based on the specific context and objectives of the risk assessment .

The subsequent sections delve into the nuances of each methodology, providing a detailed analysis of their application, usefulness, and potential drawbacks.

This exploration endeavors to equip readers with the necessary knowledge to make informed decisions when choosing the most suitable risk assessment methodology for their needs and circumstances.

What is a Risk Assessment Methodology

A risk assessment methodology is a systematic approach used to identify, evaluate, and manage potential hazards or risks in a given environment, playing a crucial role in the decision-making process.

This methodology is a key component of risk management , as it helps organizations prioritize their resources effectively.

The risk assessment process typically involves a qualitative method, which involves subjective judgment based on expert opinion, and a quantitative method, which is numerical and involves statistical data.

These methods form the basis of different types of risk analysis, which can range from simple hazard identification to complex risk modelling.

The purpose of the methodology is not only to identify potential risks but also to estimate potential impacts, thereby aiding in the development of robust mitigation strategies.

Business associates should take corrective actions to address negative risks and capitalize on positive risks. A list of risks can help with a deeper understanding and effects analysis, especially for core elements such as remote access, the supply chain, and the development life cycle.

Senior management at financial institutions should ensure that additional controls are in place to meet acceptable levels of risk at the organizational and executive levels.

A detailed report with additional guidance can provide a more accurate risk assessment and help determine risk acceptability criteria, including simple risk assessment and residual risk acceptance criteria.

Companies should consider risk avoidance and various risk treatment options to manage information security risk. Sources for risk analysis, including bicyclist risk assessment methods and critical risk elements, should be incorporated into the project risk assessment report.

The highest-level risks should be identified, with a plan for verification and effective action plans. Implementation plans and security assessment plans should be included in the security plan, with consideration for the impact cost and potential threat events.

Framework and guidance documents can be helpful for larger companies to establish a company culture that prioritizes risk management and addresses high-risk failure modes.

Experts recommend considering potential failures during development and using a digital template for risk assessments . Finally, it’s important to proofread and check for spelling, grammar, and punctuation errors before sharing or downloading the document.d Template

Quantitative

Quantitative approaches to evaluating potential hazards employ numerical data and statistical methods, providing precision and objectivity in the analysis.

This type of risk assessment methodology, or quantitative risk assessment , uses specific metrics such as risk matrix, risk levels, and risk values to measure a given risk’s potential consequences and financial impact.

– Quantitative Risk Assessment: – Risk Matrix: This tool prioritizes risks based on their likelihood and potential impact, thus enabling more focused risk management.

– Risk Levels and Values: These metrics provide numerical risk estimates, which can be used to determine the severity and financial implications of potential risks.

The quantitative risk analysis process is a critical component of comprehensive risk management, offering an empirical approach to understanding and mitigating risks.

Qualitative

Contrasting with the numeric approach, qualitative strategies involve a more subjective and interpretive evaluation of potential hazards, relying on expert judgment rather than strictly numerical data.

The qualitative risk assessment method is often employed to identify and categorize kinds of risks in a less structured, more exploratory manner.

The qualitative assessment is central to effective risk communication as it allows for nuanced descriptions of potential hazards and their effects.

Employing a qualitative analysis when assessing risk provides a comprehensive understanding of the various aspects of potential hazards, including their causes, impacts, and possible mitigation strategies.

In essence, the qualitative approach to risk assessment offers an in-depth, interpretive insight into potential risks beyond what can be ascertained from a purely numerical evaluation.

Semi-Quantitative

Semi-quantitative strategies offer a unique approach to investigating potential hazards by bridging the gap between numeric and interpretive evaluations.

This risk assessment guide outlines the semi-quantitative method, which combines aspects of both qualitative and quantitative techniques to provide a more comprehensive guide to risk assessment methodology.

It allows for evaluating risks based on numerical scores and descriptive categories, aiding the risk management process.

Semi-quantitative methods facilitate risk mitigation strategies by assigning a numerical value to the severity of potential threats. These methods improve the understanding of the potential consequences, enhancing the ability to manage and mitigate the threats.

Asset-Based

Asset-based approaches are of utmost importance in the field of hazard investigation. They provide a focused evaluation and identification of assets susceptible to potential threats.

This methodology is typically employed during the risk assessment phase of a formal risk assessment process.

The approach entails systematically identifying potential hazards associated with the asset and analyzing the residual risks . This data is then used to populate a risk management file, which serves as a comprehensive repository of risks associated with the asset.

Asset-based risk assessment methodology provides a structured framework for identifying potential risks and formulating appropriate mitigation strategies.

The method’s inherent focus on asset-specific hazards ensures a comprehensive and detailed risk profile, aiding overall risk management .

Vulnerability-Based

Shifting the focus from assets to vulnerabilities, the vulnerability-based approach to hazard investigation concentrates on the weaknesses that potential threats could exploit.

This security risk assessment method emphasizes identifying vulnerabilities within a system, which can be subjected to unauthorized access or potential incidents.

This risk analysis approach necessitates an in-depth evaluation of the system’s security controls and their effectiveness in mitigating risks. It involves determining the potential impact levels of various threats exploiting existing vulnerabilities.

The ultimate goal is to strengthen these weak points to prevent any potential breaches, thus enhancing the overall security posture.

This proactive measure is crucial in the ever-evolving landscape of security threats, ensuring a comprehensive and robust risk management strategy .

Threat-Based

Contrasting the vulnerability-based approach, the threat-based perspective of analyzing security prioritizes potential dangers that could compromise information systems’ integrity , confidentiality, or availability.

This methodology focuses not only on the assessment of risk but also on the business impact analysis of potential security incidents.

The threat-based approach involves rigorous penetration testing to simulate security risks and identify possible weak points. It anticipates environmental threats that can negatively affect the system and measures to prevent inappropriate access.

Understanding potential threats is vital in this approach as it helps create effective risk mitigation strategies.

Therefore, the threat-based methodology provides a more comprehensive and strategic approach to risk assessment in information systems, ensuring a secure and stable system.

Choosing the Right Methodology

Determining the most suitable approach for evaluating information system security necessitates a thorough understanding of the distinct characteristics and benefits of vulnerability and threat-based methodologies.

Selecting a comprehensive guide to risk assessment methodology is pivotal in the risk management cycle. It is the cornerstone for achieving an effective risk treatment process.

Factors influencing the choice of methodology include the nature of the entity undergoing evaluation, the resources available, and the desired level of detail in the risk evaluation.

Scalable risk assessment methods provide flexibility, accommodating varying scopes and complexities.

The chosen methodology’s sophistication level should match the system’s complexity under scrutiny.

Therefore, choosing the right methodology is a vital step in the approach to risk management , significantly influencing the effectiveness of the risk evaluation.

Frequently Asked Questions

What are the necessary qualifications to conduct a risk assessment.

Conducting a risk assessment necessitates a thorough understanding of the subject matter, analytical skills, and proficiency in data interpretation.

Relevant qualifications may include degrees in risk management , statistics, or related fields, supplemented by professional experience.

How frequently should a risk assessment be conducted in a business environment?

The frequency of conducting a risk assessment in a business environment depends on various factors, such as changes in operational processes, the introduction of new equipment, or after an incident or accident.

What is the typical cost associated with conducting a comprehensive risk assessment?

The cost of conducting a comprehensive risk assessment can vary widely, dependent on factors such as the industry, size of the organization, and complexity of operations. Typically, it ranges from $10,000 to $50,000.

How does the risk assessment methodology change in different industries?

Risk assessment methodologies vary across industries due to differing risk factors and regulatory requirements. Industries with higher inherent risks , such as mining or construction, may employ more robust and thorough assessment techniques.

What are some common mistakes to avoid when implementing a risk assessment methodology?

Common mistakes when implementing a risk assessment methodology include: overlooking potential risks, failing to update risk assessments regularly, not involving all stakeholders, and neglecting to incorporate risk mitigation strategies into business planning.

Knowing the pros and cons of each risk assessment method is crucial for making the right decision.

Quantitative, qualitative, semi-quantitative, asset-based, vulnerability-based, and threat-based methodologies each offer distinct advantages for different contexts.

It is essential to select a methodology that matches the precise needs of the risk assessment to guarantee a complete, precise, and valuable assessment of probable risks.

Chris Ekai is a Risk Management expert with over 10 years of experience in the field. He has a Master’s(MSc) degree in Risk Management from University of Portsmouth and is a CPA and Finance professional. He currently works as a Content Manager at Risk Publishing, writing about Enterprise Risk Management, Business Continuity Management and Project Management.

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© 2024 Risk Management

• Open access
• Published: 24 April 2024

Assessment of the measurement properties of the Peabody Developmental Motor Scales-2 by applying the COSMIN methodology

• Yuanye Zhu   ORCID: orcid.org/0009-0007-9947-401X 1 ,
• Jiahui Hu   ORCID: orcid.org/0009-0004-8878-1485 1 ,
• Weibing Ye   ORCID: orcid.org/0000-0002-7352-5372 1 ,
• Mallikarjuna Korivi   ORCID: orcid.org/0000-0002-4038-1368 1 &
• Yongdong Qian   ORCID: orcid.org/0009-0007-7535-7660 1  

Italian Journal of Pediatrics volume  50 , Article number:  87 ( 2024 ) Cite this article

Metrics details

The Peabody Developmental Motor Scales-2 (PDMS-2) has been used to assess the gross and fine motor skills of children (0–6 years); however, the measurement properties of the PDMS-2 are inconclusive. Here, we aimed to systematically review the measurement properties of PDMS-2, and synthesize the quality of evidence using the Consensus-based Standards for the Selection of Health Measurements Instruments (COSMIN) methodology. Electronic databases, including PubMed, EMBASE, Web of Science, CINAHL and MEDLINE, were searched for relevant studies through January 2023; these studies used PDMS-2. The methodological quality of each study was assessed by the COSMIN risk-of-bias checklist, and the measurement properties of PDMS-2 were evaluated by the COSMIN quality criteria. Modified GRADE was used to evaluate the quality of the evidence. We included a total of 22 articles in the assessment. Among the assessed measurement properties, the content validity of PDMS-2 was found to be sufficient with moderate-quality evidence. The structural validity, internal consistency, test-retest reliability and interrater reliability of the PDMS-2 were sufficient for high-quality evidence, while the intrarater reliability was sufficient for moderate-quality evidence. Sufficient high-quality evidence was also found for the measurement error of PDMS-2. The overall construct validity of the PDMS-2 was sufficient but showed inconsistent quality of evidence. The responsiveness of PDMS-2 appears to be sufficient with low-quality evidence. Our findings demonstrate that the PDMS-2 has sufficient content validity, structural validity, internal consistency, reliability and measurement error with moderate to high-quality evidence. Therefore, PDMS-2 is graded as ‘A’ and can be used in motor development research and clinical settings.

Introduction

Motor development refers to the ability of children to move and interact with the environment and is very important in early childhood [ 1 ]. Proper motor development provides an opportunity for children to explore and participate in the world around them [ 2 ]. Several studies have shown that motor development is closely associated with children’s cognitive ability [ 3 ], language [ 4 ], executive functioning [ 5 ], and quality of life [ 6 ]. Children with poor motor development reportedly have poor academic performance as well as depression and anxiety [ 7 ]. In addition, impaired motor development in early childhood can impact learning abilities, which may persist through adolescence or even later in life [ 8 ]. Motor disorders in children are associated with a lower quality of life in several domains, including physical, cognitive, emotional and social functioning [ 6 ]. Children with motor dyspraxia (developmental disorder) require motor intervention to promote their motor skills and to prevent postural abnormalities [ 9 ]. Therefore, early prediction of motor function is important for further intervention and education [ 10 ]. Many assessment instruments or scales have been developed to accurately and efficiently screen for motor development problems in children [ 11 , 12 ]. The Peabody Developmental Motor Scales-2 (PDMS-2) is widely used in paediatric practice and research studies to assess the gross and fine motor skills of children from birth to 6 years of age [ 13 ]. The PDMS-2 has been improved and updated based on reviews of the PDMS, comments and queries from the testers and the authors’ own experiences [ 14 ]. The key changes in PDMS include the collection of a more representative sample, the introduction of a different test structure and more specific scoring criteria [ 15 ].

The measurement properties of an instrument were described and defined by the COnsensus-based Standards for the selection of health Measurements INstruments (COSMIN). According to the COSMIN methodology, reliability, validity and responsiveness are the main domains. The reliability was categorized into test-retest, interrater and intrarater reliability, and validity was categorized into content, construct (structural, cross-cultural, hypothesis testing) and criterion validity [ 16 ]. Since the publication of PDMS-2, many studies have examined the measurement properties of this scale. The measurement properties of the original version have been assessed by English-speaking countries [ 17 , 18 , 19 ], while the measurement properties of the translated versions have been assessed by non-English-speaking countries [ 20 , 21 ]. Although several studies have confirmed the reliability and validity of the PDMS-2 device to be sufficient, there are some contradictory reports on its reliability and validity. For example, the concurrent validity of the PDMS-2 and the Bayley Scales of Infant Development II Motor Scale (BSID-II) was simultaneously reported to be “high correlation” [ 22 ] and “low correlation” [ 19 ]. Despite the heterogeneity of studies on the measurement properties of PDMS-2, no systematic review has addressed this issue. Since PDMS-2 is widely used by clinicians, therapists, psychologists and diagnosticians [ 14 ], establishing consistent evidence on its measurement properties is highly warranted.

The COSMIN methodology is typically employed to evaluate the measurement properties of various tools/scales of a certain field [ 23 , 24 ]. Hulteen et al. employed the COSMIN methodology in their systematic review of the measurement properties of several motor assessment scales in children and adolescents [ 25 ]. The COSMN methodology can also be used to review the measurement properties of a single measurement instrument, such as the Body Image Scale [ 26 ]. As reported results are inclusive of the measurement properties (reliability, validity, and responsiveness) of PDMS-2, the COSMIN could be an alternative methodology to delineate this inconsistency. Therefore, we searched for studies that determined the measurement properties of PDMS-2 and employed the COSMIN methodology to conduct a systematic review of the measurement properties of PDMS-2. In this review, we summarize the state of research on the measurement properties of PDMS-2 and synthesize the quality of evidence via the COSMIN methodology.

Literature search strategy

The PubMed, EMBASE, Web of Science, CINAHL and MEDLINE databases were searched for relevant studies that assessed the different measurement properties of PDMS-2 through January 2023. The search terms or keywords used to identify the name of the scale/instrument (PDMS-2) were “Peabody developmental motor scales-2” OR “PDMS-2” OR “Peabody developmental motor scales-second edition” OR “Peabody developmental motor scales-2nd “. The search term utilized to determine the scale measurement properties was a filter developed by the Patient Reported Outcome Measures (PROMs) Group at the University of Oxford (a high-sensitivity search filter that has been validated by Terwee et al. [ 27 ]. For the article search, we followed the latest version of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA, 2020) guidelines [ 28 ]. The full texts of the selected articles were downloaded from the journal’s homepage. In addition, we contacted our university library or external collaborators for the full-text articles upon necessary. The study protocol was registered in PROSPERO ( https://www.crd.york.ac.uk/prospero/ ; CRD42022376335).

Inclusion and exclusion criteria

The included literature met the following criteria: (1) the study was conducted on children aged 0–6 years; (2) the study addressed the evaluation of the PDMS-2 measurement properties; and (3) at least one of the scale’s measurement properties was evaluated in the study. The measurement properties of the PDMS-2 include content validity, structural validity, internal consistency, cross-cultural validity/measurement invariance, reliability, measurement error, criterion validity, hypothesis testing for construct validity, and responsiveness. The collected literature was excluded if it met any of the following criteria: (1) used PDMS-2 to investigate children’s motor development; (2) used PDMS-2 to assess the effectiveness of an intervention; (3) was a review and systematic review; or (4) had only an abstract without a full-text article or nonpeer review.

Literature selection and data extraction

The literature search, article selection and data extraction were independently performed by two researchers (YZ and JH), and the results were compared with the help of another author (YQ). Any disagreements were resolved by discussion with other review authors (WY and MK). The literature was imported into EndNote, and duplicates were first excluded. Subsequently, the titles and abstracts of the collected articles were read, and irrelevant articles were excluded. The full texts of the remaining articles were subsequently read and screened according to our study criteria.

The following information was extracted from the literature: first author name, year of publication, studied population and source, region, sample size, age and sex of the children, use of the PDMS-2 language, measurement properties of the PDMS-2 (content validity, structural validity, internal consistency, cross-cultural validity/measurement invariance, reliability, measurement error, criterion validity, hypothesis testing for construct validity, and responsiveness), and data on the measurement properties.

Evaluation of the risk of bias and quality of evidence of the included studies

We used the COSMIN risk of bias checklist [ 29 ] to assess the methodological quality of the studies. The checklist consists of ten sections, including “PROM development, content validity, structural validity, internal consistency, cross-cultural validity/measurement invariance, reliability, measurement error, criterion validity, hypothesis testing for construct validity, and responsiveness”. Appropriate boxes were selected according to the measurement properties of the study. The methodological quality of the studies was assessed as “very good”, “adequate”, “doubtful” or “inadequate” on an item-by-item basis according to the standard score given in the boxes. The overall methodological quality rating of the studies was based on the “worst score principle”. The worst score of the criteria in the box was regarded as the overall methodological quality rating of the study.

The quality of evidence was synthesized according to the modified version of the Grading of Recommendations Assessment, Development and Evaluation (GRADE) method [ 24 ]. This method is an improvement on the original version to accommodate the COSMIN method. The evidence levels could be categorized as “high”, “moderate”, “low” or “very low” according to the standard. The starting level of evidence for the included studies was “high”, and the data were subsequently downgraded according to the characteristics of the included studies. Unlike the original GRADE method, the modified version removes the “publication bias” factor. The quality of evidence was downgraded according to the risk of bias, inconsistency, indirectness, and imprecision.

Overall rating of the measurement properties

The overall rating of each measurement property of the PDMS-2 was assessed by the COSMIN methodology for systematic reviews of the PROM user manual (COSMIN manual) [ 30 ] and the COSMIN methodology for assessing the content validity of the PROM user manual [ 31 ]. The items included “content validity, structural validity, internal consistency, cross-cultural validity/measurement invariance, reliability, measurement error, criterion validity, hypothesis testing for construct validity, and responsiveness” (Table S1 ). The reported items for each measurement property were rated as “sufficient (+), “insufficient (-), or “indeterminate (?)” (Table S2 ). The overall rating of each measurement property was given as “sufficient (+)”, “insufficient (-)”, “inconsistent (±)”, or “indeterminate (?)”. Inconsistent results were analysed in groups to explore the reasons for this difference.

For reliability, studies were considered sufficient if the Pearson correlation coefficient [ 32 ] or Spearman’s rho correlation coefficient [ 33 ] was ≥ 0.80. Hypothesis testing for construct validity requires the reviewer team to set hypotheses in advance. The hypothesis for this study was as follows: for construct convergent or concurrent validity, the correlation coefficient was expected to be ≥ 0.50 for the correlations with the comparator instrument if a similar construct was measured with respect to the PDMS-2. Construct validity was rated as sufficient (+) if at least 75% of the results were in accordance with the hypotheses, insufficient (−) if at least 75% of the results were not, or indeterminate (?) if no hypotheses were defined.

Literature search results

From our database search, we identified a total of 529 articles, including 95 articles from PubMed, 103 from EMBASE, 156 from Web of Science, 48 from CINAHL, and 127 from MEDLINE. The search was performed until January 31, 2023, without restriction of early publication time.

All identified articles were imported to EndNote, and 424 duplicates were removed. The titles and abstracts of the remaining 105 articles were screened, and 68 irrelevant articles were excluded, resulting in 37 additional articles. Then, two articles were excluded due to unavailability of the full text (conference abstracts), and 35 were assessed for eligibility. We further excluded 13 articles for the following reasons: three articles were reviews [ 15 , 34 , 35 ], one was a dissertation [ 36 ], one study did not investigate the measurement properties of PDMS-2 [ 37 ], and eight studies used PDMS-2 to assess other scales [ 2 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ]. Finally, 22 articles were included in our assessment. The detailed selection process and number of articles in each step are shown in Fig.  1 .

Flow diagram of the article selection according to the PRISMA

Characteristics of the included studies

The characteristics of the included articles are shown in Table  1 . The studies were intercontinental, mainly from Europe [ 21 , 45 , 46 , 47 , 48 , 49 , 50 ], followed by Asia [ 20 , 22 , 51 , 52 , 53 , 54 , 55 ] and North America [ 14 , 17 , 18 , 19 , 56 , 57 ]. Specifically, six studies were from the USA [ 14 , 17 , 18 , 19 , 56 , 57 ]; four studies were from Taiwan, China [ 51 , 52 , 53 , 54 ]; three, Portugal [ 21 , 45 , 46 ]; two, Brazil [ 58 , 59 ]; two, South Korea [ 20 , 55 ]; one, Belgium [ 47 ]; Spain [ 50 ]; the Netherlands [ 48 ]; Iran [ 22 ]; and the UK [ 49 ]. The participants in these studies were both normal [ 14 , 19 , 20 , 21 , 45 , 46 ] and exceptional [ 17 , 18 , 22 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 ] children. Exceptional children were identified as having various disabilities, such as developmental delays [ 17 , 47 , 48 , 51 , 52 , 56 , 57 ], premature birth [ 49 , 58 , 59 ] and neurological diseases [ 18 , 50 , 53 , 54 , 55 ]. The age of the children ranged from 0 to 6 years.

Synthesis of evidence for the measurement properties of PDMS-2

The overall assessment of the PDMS-2 measurement properties and the corresponding quality of evidence for each measurement property are shown in Table  2 . The detailed quality of evidence data are provided in the supplementary material (Table S3 ).

Content validity

Of the 22 included articles, only one study methodologically assessed the content validity of the PDMS-2 standard recommended by the COSMIN [ 59 ]. The study systematically assessed the content validity of the PDMS-2 by interviewing experts in the field and judged the relevance and comprehensiveness of the scale. The overall rating of the results for content validity was found to be sufficient, and the quality of evidence was moderate. Since this study did not report comprehensibility, it was not possible to judge the overall rating of comprehensibility (Table  2 ).

Structural validity

Four of the 22 included articles assessed the bifactor structural validity of the PDMS-2 by classical test theory (CTT) [ 14 , 21 , 45 , 59 ]. The overall rating of the results for structural validity was found to be sufficient. The quality of evidence was high, and all studies were judged as very good (Table  2 ).

Internal consistency

Two studies examined the unidimensionality of the PDMS-2 subscales through Rasch analysis and indicated that most items on the scale met the unidimensionality requirement [ 52 , 58 ]. Four of the 22 included articles assessed the internal consistency of the PDMS-2 [ 21 , 45 , 50 , 54 ]. The Cronbach’s alpha values for the internal consistency of PDMS-2 were 0.999 (Reflex), 0.86–0.999 (Stationary), 0.89–0.999 (Locomotion), 0.87–0.991 (Manipulation), 0.76–0.999 (Grasping) and 0.89–0.999 (Visual–Motor). The overall rating was sufficient, and the quality of evidence of all included studies was high for internal consistency (Table  2 ).

Cross-cultural validity/measurement invariance

Of the 22 included articles, only one study assessed the cross-cultural validity of the PDMS-2 [ 46 ]. However, the methodology used in this study did not meet the COSMIN methodological requirements.

• Reliability

Ten studies assessed the reliability of the PDMS-2 [ 14 , 20 , 22 , 45 , 48 , 50 , 53 , 54 , 55 , 59 ]. According to the COSMIN manual [ 30 ], these studies can be divided into test-retest reliability, interrater reliability and intrarater reliability.

Eight studies assessed the test-retest reliability of the PDMS-2 [ 14 , 22 , 45 , 48 , 53 , 54 , 55 , 59 ]. These studies mainly used the intraclass correlation coefficient (ICC) [ 22 , 45 , 53 , 54 ], Pearson correlation coefficient (r) [ 14 , 55 , 59 ] and Spearman’s rho correlation coefficient (ρ) [ 48 ] to judge test-retest reliability. The ICCs for the test-retest reliability of the PDMS-2 were 0.75–0.99 (gross motor subscale [GMS]) and 0.71–0.99 (fine motor subscale [FMS]). The Pearson correlation coefficients were 0.84–0.99 (GMS) and 0.73–0.99 (FMS); the Spearman’s rho correlation coefficients were 0.84–0.98 (FMS). The overall rating of the results for test-retest reliability was found to be sufficient, and the quality of evidence was high (Table  2 ).

As shown in Table  2 , five studies assessed the interrater reliability of the PDMS-2 [ 14 , 20 , 48 , 50 , 59 ]. These studies mainly used the ICC [ 20 , 50 , 59 ], Pearson correlation coefficient [ 14 ] and Spearman’s rho correlation coefficient [ 48 ] to judge interrater reliability. The ICCs for the interrater reliability of the PDMS-2 were 0.758–0.920 (Reflex), 0.985–0.999 (Stationary), 0.990-1.000 (Locomotion), 0.972–0.999 (Manipulation), 0.941–0.991 (Grasping) and 0.988-1.000 (Visual-motor); the Pearson correlation coefficient was 0.97 (GMS), 0.98 (FMS) and 0.96 (Total Motor scale); and the Spearman’s rho correlation coefficients were 0.94–0.99 (FMS). The overall rating of the results for the interrater reliability was found to be sufficient. The quality of evidence of the studies was judged to be high, and all studies were identified as very good.

One study assessed the intrarater reliability of the PDMS-2 [ 59 ]. The ICC for the intrarater reliability of the PDMS-2 was more than 0.70. However, due to the imprecision of the included studies (total sample size 80, i.e., < 100), the quality of evidence was graded as moderate. Therefore, there was sufficient moderate-quality evidence for the intrarater reliability of the PDMS-2 (Table  2 ). Taken together, the high-quality evidence from our assessment demonstrated that the reliability of the PDMS-2 was sufficient.

Measurement error

One study evaluated the measurement error of PDMS-2 [ 54 ]. The smallest detectable change (SDC) was 7.76, and the minimal important change (MIC) was 8.39, which met the criterion of sufficient survival (+, SDC < MIC). The quality of evidence was high. Therefore, there was sufficient high-quality evidence for the measurement error of PDMS-2 (Table  2 ).

Hypothesis testing for construct validity

There is no ‘gold standard’ in the field of children’s motor development assessment. Therefore, concurrent validity as a part of criterion validity is classified as evidence of construct validity recommended by the COSMIN [ 30 ].

A total of 13 studies evaluated the construct validity of the PDMS-2 [ 17 , 18 , 19 , 20 , 22 , 47 , 48 , 49 , 51 , 54 , 56 , 57 ]. These studies assessed the construct validity of the PDMS-2 by examining the correlation of the PDMS-2 with similar domain measurement instruments. These measurement instruments included the Early Intervention Developmental Profile (EIDP) [ 17 ], Miller Function and Participation Scales (M-FUN) [ 18 ], the Bayley Scales of Infant and Toddler Development, 3rd edition (Bayley-III) [ 49 , 51 , 56 ], the Bayley Scales of Infant Development-II (BSID-II) Motor Scale [ 19 , 22 , 57 ], the Bruininks-Oseretsky Test of Motor Proficiency-Second Edition (BOT-2) [ 20 , 54 ] and the Movement Assessment Battery for Children (M-ABC) [ 47 , 48 ] (Table  2 ).

One study assessed the concurrent validity of the PDMS-2 Gross Motor scale (PDMS-GM-2) with the EIDP [ 17 ]. The overall rating results showed that the concurrent validity was sufficient. Because the sample size (30 children) was less than 50, the quality of evidence was low. Overall, there was sufficient low-quality evidence for the concurrent validity of the PDMS-GM-2 with the EIDP. One study assessed the concurrent validity of the PDMS-GM-2 with the M-FUN [ 18 ]. The overall rating results showed that the concurrent validity was sufficient, but the quality of evidence was low due to the small sample size (22 children, i.e., < 50). Overall, our results showed that there was sufficient low-quality evidence for the concurrent validity of the PDMS-GM-2 with M-FUN (Table  2 ).

Three studies assessed the concurrent validity of the PDMS-2 with the Bayley-III [ 49 , 51 , 56 ]. The overall rating of the results for the concurrent validity of the PDMS-2 with the Bayley-III was found to be sufficient, and the quality of the evidence was high. Three studies assessed the concurrent [ 19 , 57 ] and convergent [ 22 ] validity of the PDMS-2 with the BSID-II. Of these three studies, two involved the recruitment of exceptional children [ 22 , 57 ]; the overall rating was judged as sufficient (+), and the quality of evidence was high. One study recruited normally developing children [ 19 ]; the overall rating was judged as insufficient (-), and the quality of evidence was low. Our assessment revealed that the results of the PDMS-2 device with BSID-II appeared to be sufficient, and the quality of evidence was high (Table  2 ).

Two studies assessed the concurrent validity of the PDMS-2 with the BOT-2 [ 20 , 54 ]. The overall rating of the results for the concurrent validity of the PDMS-2 with the BOT-2 was found to be sufficient, and the quality of evidence was high. Furthermore, two studies [ 47 , 48 ] examined the convergent validity of PDMS-2 with M-ABC. These two studies met the requirement of correlation in PDMS-GM-2 but not in PDMS-FM-2. Therefore, the convergent validity of PDMS-2 with M-ABC was inconsistent. The quality of evidence was very low due to the small sample size (67 children, < 100) and inconsistent results. Thus, there is inconsistent very low-quality evidence for the convergent validity of PDMS-2 with M-ABC (Table  2 ).

• Responsiveness

Two studies assessed the responsiveness of PDMS-2 [ 53 , 54 ]. The overall rating of the results was sufficient. However, the quality of evidence was low because the study was severely biased according to the COSMIN risk of bias assessment checklist [ 29 ]. These results indicate that even low-quality evidence showed sufficient responsiveness of PDMS-2 (Table  2 ).

To the best of our knowledge, this is the first systematic review in which the COSMIN methodology was used to assess the measurement properties of PDMS-2. In this study, we evaluated the different properties of PDMS-2, which were reported in 22 articles. According to the COSMIN manual, any measurement instrument or scale with sufficient evidence for content validity (any level quality) or internal consistency (at least low quality) can be categorized as “A” [ 30 ]. Our results showed that the content validity of the PDMS-2 had sufficient moderate-quality evidence, and the internal consistency of the PDMS-2 had sufficient high-quality evidence. These findings revealed that PDMS-2 can be graded as ‘A’, which can be used in motor development research and in clinical settings. The COSMIN manual further states that the results obtained from any “A” grade scale can be trusted [ 30 ].

According to the COSMIN manual, content validity is the most important property of a measurement instrument or scale [ 30 ]. Bums and Grove stated that content validity is obtained from three sources: literature, patient judgement (judgement of representatives of the relevant populations), and expert judgement [ 60 ]. The most commonly used source of content validity is expert judgement [ 61 ], and the COSMIN method combines patient judgement with expert judgement to assess three parts of content validity: relevance, comprehensiveness, and comprehensibility [ 30 ]. In our assessment, only one study reported the content validity of the PDMS-2 [ 59 ]. However, in this study we examined the content validity of the PDMS-2 by asking experts in related fields but not patients/participants [ 59 ]. When using the PDMS-2, patients (children) must complete their movements only following the instructions of the evaluator and do not need to understand the meaning of the PDMS-2 items [ 14 ]. Therefore, no studies assessing the comprehensibility of PDMS-2 were found, but we still consider the content validity of PDMS-2 to be sufficient.

For the assessment of structural validity, the COSMIN quality criterion includes two criteria, namely, CTT and item response theory (IRT) [ 30 , 62 ]. All the studies addressing structural validity in our analyses used the CTT method. Although the CTT easily assesses structural validity, the results from the IRT are said to be more reliable in educational and psychometric fields [ 63 ]. Due to its high accuracy, IRT is a highly validated method for assessing the structural validity of PDMS-2 [ 63 ]. However, at present, no study has used the IRT to evaluate the structural validity of the PDMS-2, and further studies are necessary to address the importance of IRT.

According to the COSMIN manual, cross-cultural validity/measurement invariance has been defined as “the degree to which the performance of the items on a translated or culturally adapted measurement instruments are an adequate reflection of the performance of the items of the original version of the measurement instruments” [ 30 ]. In our analyses, we determined that no studies have assessed the cross-cultural validity/measurement invariance of the PDMS-2 by the COSMIN recommended method. We suggest further research on the cross-cultural validity/measurement invariance of the PDMS-2.

The results of the construct validity test demonstrated that the PDMS-2 is well correlated with most of the same-domain measurement instruments. However, the results of the three studies of the PDMS-2 device with BSID-II differed, which might be due to differences in sample type. Of these three studies, one study recruited normally developing children [ 19 ], and two studies recruited exceptional children [ 22 , 57 ]. The concurrent validity of the PDMS-2 with the BSID-II among normal children was insufficient because of the small sample size ( n  = 15, i.e., < 50) [ 19 ]. However, the concurrent or convergent validity among exceptional children was found to be sufficient for obtaining high-quality evidence (sample size 198, > 100) [ 22 , 57 ]. The COSMIN stated that high-quality studies provide stronger evidence than low-quality studies and can be considered decisive in determining the overall rating when ratings are inconsistent [ 30 ]. Overall, our findings revealed that the results of the assessment of PDMS-2 with BSID-II were sufficient. Next, we addressed the convergent validity of the PDMS-2 and M-ABC devices in two studies [ 47 , 48 ]; the results were sufficient for the gross motor quotient (GMQ) and inconsistent for the fine motor quotient (FMQ). As the sample size was small and the assessment ratings were inconsistent, the quality of PDMS-2 and M-ABC was considered very low evidence.

The risk of bias of reliability and measurement error was not judged according to the retest interval recommended by the COSMIN risk of bias checklist (approximately two weeks) due to the rapid growth rate of children aged 0 to 6 years. However, we judged the risk of bias in the studies (approximately one week) using another method described by Lee et al. [ 32 ]. A suitable measurement error requires that the smallest detectable change (SDC) in the measurement instrument is less than the MIC [ 64 ]. Only one study was conducted on the SDC and MIC [ 54 ]. The MIC is the best result that can be calculated from multiple studies and using multiple anchors [ 65 ]. Therefore, it is clear that one study alone is not convincing and involves multiple anchors, and we suggest further studies to verify the MIC results.

Responsiveness measures the ability of a scale to change over time in the construct to be measured [ 30 ]. The results of the two included studies [ 53 , 54 ] showed sufficient responsiveness of PDMS-2, but the quality of evidence of these two studies was low. There are two reasons for these results. First, these two studies did not describe the intervention details. The second reason is that Wang et al. [ 53 ] used a statistical method (Guyatt’s responsiveness ratio), which is not recommended by COSMIN [ 30 ]. According to the COSMIN manual, Guyatt’s responsiveness ratio takes the minimal important change into account [ 30 ]. A marginally important change concerns the interpretation of the change score, not the validity of the change score [ 30 ]. Low-quality evidence does not mean validating the sufficient or insufficient responsiveness of the PDMS-2 before and after the intervention.

In addition to the abovementioned outcome measures in COSMIN, interpretability and feasibility are also important variables for evaluating the measurement properties of PDMS-2 [ 30 ]. In our assessment, one study [ 54 ] reported no ceiling or floor effects when using the PDMS-2 to assess the motor development of children. Reporting such no ceiling or floor effects indicates good interpretability of the PDMS-2. According to the results of previous studies of PDMS-2 [ 14 ], we assumed that the use of PDMS-2 is highly feasible and that a specific environment and/or equipment are not necessary to assess motor development in children.

The synthesized evidence of the measurement properties of PDMS-2 is comparable to that of other well-known similar domain measurement instruments, such as M-ABC, BOT-2, Bayley-III, and BSID-II. For instance, a previous study reported that the interrater reliability, test-retest reliability and content validity of the M-ABC were good, but mixed results were reported for internal consistency and cross-cultural validity [ 66 ]. The BOT-2 scale was reported to have excellent interrater reliability, test-retest reliability, and internal consistency [ 66 ]. Another study reported that the internal consistency and test-retest reliability of the Bayley-III were good [ 35 ]. In addition, the interrater reliability, internal consistency, and test-retest reliability of the BSID-II were reported to be sufficient [ 67 ]. Our findings demonstrate that the PDMS-2 has sufficient content validity, structural validity, internal consistency, reliability and measurement error with moderate to high-quality evidence.

Limitations and future perspectives

Our results could not establish the quality of evidence for the cross-cultural validity of PDMS-2 because few or no studies have assessed the cross-cultural validity of PDMS-2 via the COSMIN-recommended methodology. For the article search, the Cochrane reviews used various additional sources, including dissertations, editorials, and conference proceedings. However, the probability of finding additional relevant articles for systematic reviews from these sources appears to be low [ 24 ]. As we excluded the nonpeer reviewed articles in our study, our conclusions may not be influenced by these articles; however, we cannot completely exclude them.

To date, no study has addressed the cross-cultural validity of PDMS-2 by the COSMIN recommended method. In addition, only one study assessed the measurement error of PDMS-2. Therefore, further studies are necessary to assess the cross-cultural validity and measurement error of PDMS-2. These measurement properties can be used in the assessment to determine the overall rating and quality of evidence by the COSMIN methodology. We further suggest that future studies on the responsiveness of PDMS-2 that can be used in the COSMIN methodology.

Conclusions

Assessment results from the COSMIN methodology showed that the PDMS-2 has sufficient high-quality evidence for structural validity and internal consistency. The reliability and measurement error of the PDMS-2 also demonstrated sufficient high-quality evidence. However, no adequate or low-quality evidence was found for the cross-cultural validity/measurement invariance and responsiveness of the PDMS-2. On the other hand, very low-quality evidence for convergent validity suggested that the PDMS-FM-2 was inconsistently correlated with the M-ABC, which needs to be further investigated. Overall, our findings revealed that the PDMS-2 was graded as “A”, and this scale can be used in the field of child motor development research as well as in clinical settings.

Data availability

All the data that support the findings of this study are available from the corresponding author upon reasonable request.

Abbreviations

the COnsensus based Standards for the selection of health Measurements INstruments

2 Peabody Developmental Motor Scales-2

Early Intervention Developmental Profile

the Bayley Scales of Infant and Toddler Development, 3rd edition

the Bayley Scales of Infant Development II Motor Scale

BOT-2 Bruininks-Oseretsky Test of Motor Proficiency-Second Edition

Miller Function and Participation Scales

Movement Assessment Battery for Children

Leo I, Leone S, Dicataldo R, Vivenzio C, Cavallin N, Taglioni C, et al. A non-randomized pilot study on the benefits of Baby Swimming on Motor Development. Int J Environ Res Public Health. 2022;19:9262.

Article   PubMed   PubMed Central   Google Scholar  

Libertus K, Landa RJ. The Early Motor Questionnaire (EMQ): a parental report measure of early motor development. Infant Behav Dev. 2013;36:833–42.

Article   PubMed   Google Scholar  

van der Fels IMJ, te Wierike SCM, Hartman E, Elferink-Gemser MT, Smith J, Visscher C. The relationship between motor skills and cognitive skills in 4–16 year old typically developing children: a systematic review. J Sci Med Sport. 2015;18:697–703.

Leonard HC, Hill EL. The impact of motor development on typical and atypical social cognition and language: a systematic review. Child Adolesc Ment Health. 2014;19:163–70.

Piek JP, Dyck MJ, Nieman A, Anderson M, Hay D, Smith LM, et al. The relationship between motor coordination, executive functioning and attention in school aged children. Arch Clin Neuropsychol. 2004;19:1063–76.

Zwicker JG, Harris SR, Klassen AF. Quality of life domains affected in children with developmental coordination disorder: a systematic review. Child Care Health Dev. 2013;39:562–80.

Article   CAS   PubMed   Google Scholar  

Piek JP, Barrett NC, Smith LM, Rigoli D, Gasson N. Do motor skills in infancy and early childhood predict anxious and depressive symptomatology at school age? Hum Mov Lifesp Learn Synerg Dis. 2010;29:777–86.

Google Scholar  

Williams J, Holley P. Linking motor development in infancy and early childhood to later school learning. Aust J Child Fam Health Nurs. 2013;10:15–21.

Campbell SK, Osten ET, Kolobe THA, Fisher AG. Development of the test of Infant Motor Performance. Phys Med Rehabil Clin N Am. 1993;4:541–50.

Article   Google Scholar  

Richardson PK. Use of standardized tests in pediatric practice. Occup Ther Child. 2013;6:216–39.

Wiart L, Darrah J. Review of four tests of gross motor development. Dev Med Child Neurol. 2001;43:279–85.

Cools W, De Martelaer K, Samaey C, Andries C. Movement skill assessment of typically developing preschool children: a review of seven movement skill assessment tools. J Sports Sci Med. 2009;8:154.

PubMed   PubMed Central   Google Scholar  

Mason AN, Broussard B, Cook J, Duszkiewicz B. A review of the Peabody Developmental Motor scales–Second Edition (PDMS-2). Crit Rev Phys Rehabil Med. 2018;30:259–63.

Folio M, Fewell R. Peabody Developmental Motor Scales. 2nd edn (PDMS-2). Austin TX -Ed. 2000.

Tieman B, Palisano R, Sutlive A. Assessment of motor development and function in preschool children. Ment Retard Dev Disabil Res Rev. 2005;11:189–96.

Mokkink LB, Terwee CB, Patrick DL, Alonso J, Stratford PW, Knol DL, et al. The COSMIN study reached international consensus on taxonomy, terminology, and definitions of measurement properties for health-related patient-reported outcomes. J Clin Epidemiol. 2010;63:737–45.

Maring JR, Elbaum L. Concurrent Validity of the Early Intervention Developmental Profile and the Peabody Developmental Motor Scale-2. Pediatr Phys Ther [Internet]. 2007;19. https://journals.lww.com/pedpt/Fulltext/2007/01920/Concurrent_Validity_of_the_Early_Intervention.3.aspx .

Holloway JM, Long T, Biasini F. Concurrent validity of two standardized measures of Gross Motor function in Young Children with Autism Spectrum Disorder. Phys Occup Ther Pediatr. 2019;39:193–203.

Connolly BH, Dalton L, Smith JB, Lamberth NG, McCay B, Murphy W. Concurrent Validity of the Bayley Scales of Infant Development II (BSID-II) Motor Scale and the Peabody Developmental Motor Scale II (PDMS-2) in 12-Month-Old Infants. Pediatr Phys Ther [Internet]. 2006;18. https://journals.lww.com/pedpt/Fulltext/2006/01830/Concurrent_Validity_of_the_Bayley_Scales_of_Infant.3.aspx .

Lee J-H, Moon-Young KK-MC, Eunkyoung H. Study of Validity and Interrater Reliability of Korean Version of the Peabody Developmental Motor Scale 2. J Korean Acad Sens Integr. 2019;17:14–25.

Saraiva L, Rodrigues LP, Barreiros J. Adaptation and validation of the Portuguese Peabody Developmental Motor Scales-2 version: a study with preschoolers children. Rev Educ FísicaUEM. 2011;22:511–21.

Tavasoli A, Azimi P, Montazari A. Reliability and validity of the Peabody Developmental Motor scales-Second Edition for assessing Motor Development of Low Birth Weight Preterm infants. Pediatr Neurol. 2014;51:522–6.

Williams B, Beovich B. A systematic review of psychometric assessment of the Jefferson Scale of Empathy using the COSMIN risk of Bias checklist. J Eval Clin Pract. 2020;26:1302–15.

Prinsen Ca, Mokkink C, Bouter LB, Alonso LM, Patrick J, de Vet DL. COSMIN guideline for systematic reviews of patient-reported outcome measures. Qual Life Res. 2018;27:1147–57.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Hulteen RM, Barnett LM, True L, Lander NJ, del Pozo Cruz B, Lonsdale C. Validity and reliability evidence for motor competence assessments in children and adolescents: a systematic review. J Sports Sci. 2020;38:1717–98.

Melissant HC, Neijenhuijs KI, Jansen F, Aaronson NK, Groenvold M, Holzner B, et al. A systematic review of the measurement properties of the body image Scale (BIS) in cancer patients. Support Care Cancer. 2018;26:1715–26.

Terwee CB, Jansma EP, Riphagen II, de Vet HC. Development of a methodological PubMed search filter for finding studies on measurement properties of measurement instruments. Qual Life Res. 2009;18:1115–23.

Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Syst Rev. 2021;10:89.

Mokkink LB, de Vet HCW, Prinsen Ca, Patrick C, Alonso DL, Bouter J. COSMIN Risk of Bias checklist for systematic reviews of patient-reported outcome measures. Qual Life Res. 2018;27:1171–9.

Mokkink LB, Prinsen C, Patrick DL, Alonso J, Bouter L, de Vet HC et al. COSMIN methodology for systematic reviews of patient-reported outcome measures (PROMs). User Man. 2018;78.

Terwee CB, Prinsen C, Chiarotto A, De Vet H, Bouter LM, Alonso J, et al. COSMIN methodology for assessing the content validity of PROMs–user manual. Amst VU Univ Med Cent; 2018.

Lee J, Lee E-H, Moon SH. Systematic review of the measurement properties of the Depression anxiety stress Scales–21 by applying updated COSMIN methodology. Qual Life Res. 2019;28:2325–39.

Climent-Sanz C, Marco-Mitjavila A, Pastells-Peiró R, Valenzuela-Pascual F, Blanco-Blanco J, Gea-Sánchez M. Patient reported outcome measures of sleep quality in fibromyalgia: a COSMIN systematic review. Int J Environ Res Public Health. 2020;17:2992.

Article   PubMed Central   Google Scholar  

Mendonça B, Sargent B, Fetters L. Cross-cultural validity of standardized motor development screening and assessment tools: a systematic review. Dev Med Child Neurol. 2016;58:1213–22.

Griffiths A, Toovey R, Morgan PE, Spittle AJ. Psychometric properties of gross motor assessment tools for children: a systematic review. BMJ Open. 2018;8:e021734.

Phillips D. Concurrent validity and responsiveness of the Peabody Developmental Motor Scales-2 in infants and children with pompe disease undergoing enzyme replacement therapy. 2012.

Zhao G, Bian Y, Li M. Impact of passing items above the ceiling on the assessment results of Peabody developmental motor scales. Beijing Da Xue Xue Bao. 2013;45:928–32.

Hua J, Gu G, Meng W, Wu Z. Age band 1 of the Movement Assessment Battery for Children-Second Edition: exploring its usefulness in mainland China. Res Dev Disabil. 2013;34:801–8.

Siu AMH, Lai CYY, Chiu ASM, Yip CCK. Development and validation of a fine-motor assessment tool for use with young children in a Chinese population. Res Dev Disabil. 2011;32:107–14.

Pin TW, So VKK, Siu CSH, Yip SSN, Cheung SS, Kan JY. Development of the Social Motor function classification system for children with Autism Spectrum disorders: a psychometric study. J Autism Dev Disord. 2021;51:1995–2003.

Wang H, Li H, Wang J, Jin H. Reliability and concurrent validity of a Chinese version of the Alberta Infant Motor Scale administered to high-risk infants in China. BioMed Res Int. 2018;2018:1–10.

KANITKAR SZTURM, REMPEL, PARMAR, NAIK NARAYAN. Reliability and validity of the computer game based assessment tool for hand and arm impairments in children with neurodevelopmental disorders. Dev Med Child Neurol. 2017;59:78–78.

Kanitkar A, Parmar ST, Szturm TJ, Restall G, Rempel G, Naik N, et al. Reliability and validity of a computer game-based tool of upper extremity assessment for object manipulation tasks in children with cerebral palsy. J Rehabil Assist Technol Eng. 2021;8:205566832110140.

Mayrand L, Mazer B, Menard S, Chilingaryan G. Screening for motor deficits using the Pediatric evaluation of disability inventory (PEDI) in children with language impairment. Dev Neurorehabilitation. 2009;12:139–45.

Article   CAS   Google Scholar  

Rebelo M, Serrano J, Duarte-Mendes P, Monteiro D, Paulo R, Marinho DA. Evaluation of the Psychometric Properties of the Portuguese Peabody Developmental Motor Scales-2 Edition: a study with children aged 12 to 48 months. Children. 2021;8:1049.

Saraiva L, Rodrigues LP, Cordovil R, Barreiros J. Motor profile of Portuguese preschool children on the Peabody Developmental Motor Scales-2: a cross-cultural study. Res Dev Disabil. 2013;34:1966–73.

Waelvelde HV, Peersman W, Lenoir M, Engelsman BCMS. Convergent validity between two motor tests: Movement-ABC and PDMS-2. Adapt Phys Act Q. 2007;24:59–69.

van Hartingsveldt MJ, Cup EH, Oostendorp RA. Reliability and validity of the fine motor scale of the Peabody Developmental Motor Scales-2. Occup Ther Int. 2005;12:1–13.

Gill K, Osiovich A, Synnes A, Agnew A, Grunau J, Miller RE. Concurrent validity of the Bayley-III and the Peabody Developmental Motor Scales-2 at 18 months. Phys Occup Ther Pediatr. 2019;39:514–24.

Álvarez Gonzalo V, Pandiella Dominique A, Kürlander Arigón G, Simó Segovia R, Caballero FF, Miret M. Validation of the PDMS-2 scale in the Spanish population. Evaluation of physiotherapy intervention and parental involvement in the treatment of children with neurodevelopmental disorders. Rev Neurol. 2021;73:81.

PubMed   Google Scholar  

Lin L-Y, Tu Y-F, Yu W-H, Ho M-H, Wu P-M. Investigation of fine motor performance in children younger than 36-month-old using PDMS-2 and Bayley-III. Eur J Dev Psychol. 2020;17:746–60.

Chien C-W, Bond TG. Measurement Properties of Fine Motor Scale of Peabody Developmental Motor scales-Second Edition: a Rasch Analysis. Am J Phys Med Rehabil. 2009;88:376–86.

Wang H-H, Liao H-F, Hsieh C-L, Reliability. Sensitivity to change, and responsiveness of the Peabody Developmental Motor scales–Second Edition for Children with cerebral palsy. Phys Ther. 2006;86:1351–9.

Wuang Y-P, Su C-Y, Huang M-H. Psychometric comparisons of three measures for assessing motor functions in preschoolers with intellectual disabilities. J Intellect Disabil Res. 2012;56:567–78.

Kim B-R, Kim K-M, Chang M-Y, Hong E. Study of Construct Validity and Test-Retest reliability of the Korean Version Peabody Developmental Motor scales-Second Edition (PDMS-2). J Korean Soc Sens Integr Ther. 2021;19:32–43.

Connolly BH, McClune NO, Gatlin R. Concurrent validity of the Bayley-III and the Peabody Developmental Motor Scale–2. Pediatr Phys Ther. 2012;24:345–52.

Provost B, Heimerl S, McClain C, Kim N-H, Lopez BR, Kodituwakku P. Concurrent validity of the Bayley scales of Infant Development II Motor Scale and the Peabody Developmental Motor Scales-2 in children with Developmental Delays. Pediatr Phys Ther. 2004;16:149–56.

Valentini NC, Zanella LW. Peabody Developmental Motor Scales-2: the Use of Rasch Analysis to examine the model unidimensionality, motor function, and Item Difficulty. Front Pediatr. 2022;10:852732–852732.

Zanella LW, Valentini NC, Copetti F, Nobre GC. Peabody Developmental Motor Scales - Second Edition (PDMS-2): reliability, content and construct validity evidence for Brazilian children. Res Dev Disabil. 2021;111:103871.

Burns N, Grove S. The practice of nursing research conduct,critique, and utilization. 2nd ed. WB Saunders Co; 1993.

Almanasreh E, Moles R, Chen TF. Evaluation of methods used for estimating content validity. Res Soc Adm Pharm. 2019;15:214–21.

Terwee CB, Bot SD, de Boer MR, van der Windt DA, Knol DL, Dekker J, et al. Quality criteria were proposed for measurement properties of health status questionnaires. J Clin Epidemiol. 2007;60:34–42.

Sun D, Zheng R. Psychometric theory. Beijing: Kaiming; 2012.

de Vet HC, Terwee CB, Ostelo RW, Beckerman H, Knol DL, Bouter LM. Minimal changes in health status questionnaires: distinction between minimally detectable change and minimally important change. Health Qual Life Outcomes. 2006;4:1–5.

Yost KJ, Eton DT, Garcia SF, Cella D. Minimally important differences were estimated for six patient-reported outcomes Measurement Information System-Cancer scales in advanced-stage cancer patients. J Clin Epidemiol. 2011;64:507–16.

Eddy LH, Bingham DD, Crossley KL, Shahid NF, Ellingham-Khan M, Otteslev A, et al. The validity and reliability of observational assessment tools available to measure fundamental movement skills in school-age children: a systematic review. PLoS ONE. 2020;15:e0237919.

Nellis L, Gridley BE. Review of the Bayley scales of Infant Development—Second edition. J Sch Psychol. 1994;32:201–9.

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Acknowledgements

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This study was supported by the “Jinhua Maimiao Education Technology Co., Ltd.,” Zhejiang Province, China, in the form of a research grant (Grant number: KYH06Y21383).

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Institute of Human Movement and Sports Engineering, College of Physical Education and Health Sciences, Zhejiang Normal University, 321004, Jinhua City, Zhejiang, China

Yuanye Zhu, Jiahui Hu, Weibing Ye, Mallikarjuna Korivi & Yongdong Qian

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All the listed authors contributed to the conception and design of the study. The article search, data collection and assessments were performed by YZ and JH. The first draft of the manuscript was written by YZ, JH and YQ. All the authors participated in the data validation and revision of the draft. WY and MK revised and finalized the manuscript. All the authors read and approved the final version of the manuscript.

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Correspondence to Mallikarjuna Korivi or Yongdong Qian .

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13052_2024_1645_MOESM1_ESM.docx

Supplementary Material 1. Table S1. COSMIN Definitions of Measurement Properties. Table S2. COSMIN Criteria for Assessing the Measurement Properties. Table S3. Levels of Evidence for the Measurement Properties of the PDMS-2.

Supplementary Material 2

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Zhu, Y., Hu, J., Ye, W. et al. Assessment of the measurement properties of the Peabody Developmental Motor Scales-2 by applying the COSMIN methodology. Ital J Pediatr 50 , 87 (2024). https://doi.org/10.1186/s13052-024-01645-6

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Received : 29 August 2023

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Published : 24 April 2024

DOI : https://doi.org/10.1186/s13052-024-01645-6

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Title: abcd: trust enhanced attention based convolutional autoencoder for risk assessment.

Abstract: Anomaly detection in industrial systems is crucial for preventing equipment failures, ensuring risk identification, and maintaining overall system efficiency. Traditional monitoring methods often rely on fixed thresholds and empirical rules, which may not be sensitive enough to detect subtle changes in system health and predict impending failures. To address this limitation, this paper proposes, a novel Attention-based convolutional autoencoder (ABCD) for risk detection and map the risk value derive to the maintenance planning. ABCD learns the normal behavior of conductivity from historical data of a real-world industrial cooling system and reconstructs the input data, identifying anomalies that deviate from the expected patterns. The framework also employs calibration techniques to ensure the reliability of its predictions. Evaluation results demonstrate that with the attention mechanism in ABCD a 57.4% increase in performance and a reduction of false alarms by 9.37% is seen compared to without attention. The approach can effectively detect risks, the risk priority rank mapped to maintenance, providing valuable insights for cooling system designers and service personnel. Calibration error of 0.03% indicates that the model is well-calibrated and enhances model's trustworthiness, enabling informed decisions about maintenance strategies

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Anti-money laundering and countering the financing of terrorism at international level

The Commission is mandated to identify high-risk third countries having strategic deficiencies in their regime on anti-money laundering and countering the financing of terrorism.

What the EU is doing and why

To effectively combat the global circulation of dirty money, international efforts are needed. The Commission is actively working with international partners for instance through the Financial Action Task Force (FATF) , the global standard setter on anti-money laundering and counter terrorism financing. The FATF notably identifies jurisdictions having strategic deficiencies in their regimes to counter money laundering and terrorist financing. The EU’s listing takes into account the recommendations provided by FATF.

The identification of high-risk counties is required in order to protect the EU financial system and the proper functioning of the internal market. The Commission is empowered to identify high-risk third countries which have strategic deficiencies in their anti-money laundering and countering the financing of terrorism frameworks. This reduces the risks that could pose threats to the Union’s financial system.

In line with the Directive (EU) 2018/843 (5 th Anti-Money Laundering Directive) , gatekeepers, such as banks, are obliged to carefully consider business relationships and transactions involving high-risk third countries through increased checks and control measures defined under Article 18a of the Directive.

Latest version of the list of high-risk third countries

On 12 December 2023, the European Commission adopted a new Delegated Regulation in relation to third countries which have strategic deficiencies in their AML/CFT regimes . The Delegated Regulation amends Delegated Regulation (EU) 2016/1675 .

The following jurisdictions are identified as having strategic deficiencies in their AML/CFT regimes:

A consolidated version of the EU list is available (with only measures that already entered into force).

The listing process

Objectives of the list.

The objectives of the list can be subdivided into three main goals:

What are the steps

The listing process follows a staged approach that can be divided into four parts:

Pre-assessment to determine the countries to be assessed and identify the level of priority of their assessment, in addition to countries already listed by the Financial Action Task Force.

Assessment of the relevant 3rd countries’ anti-money laundering and counter-terrorism financing regimes, starting with countries of the highest priority.

Listing high-risk third countries that show strategic deficiencies in their anti-money laundering and counter-terrorism financing regimes.

Monitor progress of listed countries, continue monitoring of already reviewed countries, and assess additional countries.

Methodology

To ensure a fair and transparent process concerning the identification of third countries, the Commission developed a methodology in 2020 . The methodology aims to clarify the measures to identify the high-risk countries based on the faults in their national AML/CTF regimes posing significant threats to the EU’s financial system.

More information on the methodology

Planning of assessment

The Commission carried out a pre-assessment to determine relevant countries to be assessed and the level of priority, in addition to those already listed by the Financial Action Task Force. Countries are considered relevant for the EU financial system in case they meet any of the following non-cumulative criteria

• a country is identified by the European External Action Service or by Europol as having a systemic impact on the integrity of the EU financial system
• a country was reviewed as an international offshore financial centres by the International Monetary Fund
• a country is considered as economically relevant based on the strength of the economic ties with the EU and the magnitude of its financial sector

On this basis, the Commission identified 132 jurisdictions so far that will be further analyzed according to its methodology over the period 2018-2025. The list of 132 countries included in the scope .

With regard to the level of priority

• the Commission reviews as a matter of priority a first group of 54 jurisdiction (Priority 1 countries). The assessment is an ongoing exercise; hence any country will be reassessed when new relevant information sources become available
• the other jurisdictions (Priority 2 countries) will be assessed successively until 2025

Evolution of the list

Based on Directive (EU) 2015/849 and the Commission’s power of adopting delegated acts regarding high-risk third countries, the Commission adopted the following delegated acts:

Publication of  Commission Delegated Regulation (EU) 2024/163 amending the EU list.

Publication of  Commission Delegated Regulation (EU) 2023/2070 amending the EU list.

Publication of Commission Delegated Regulation (EU) 2023/1219 amending the EU list.

Publication of Commission Delegated Regulation (EU) 2023/410 amending the EU list.

Publication of Commission Delegated Regulation (EU) 2022/229 amending the EU list.

Publication of Delegated Regulation (EU) 2021/37 amending the EU list.

Revised methodology for identifying high risk third countries

• FATF lists as a baseline/ and increased synergies with FATF listing process
• additional countries based on EU own assessment based on increased engagement
• Enhanced consultation of Member States’ experts

Publication of Delegated Regulation (EU) amending the EU list

Publication of the Delegated Regulation (EU) 2018/1467 amending the EU list.

First methodology for identifying high risk third countries

• FATF lists as a baseline
• additional countries based on EU own assessment

Publication of Delegated Regulation (EU) 2018/212 amending the EU list.

Publication of Delegated Regulation (EU) 2018/105 amending the EU list.

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First EU list – based on FATF lists ( Delegated Regulation (EU) 1675/2016) .

Relevant legislation

Anti-money laundering and terrorist financing directive iv (amld iv) - 2015/849/eu, basic information.

• Text of the AMLD IV (2015/849/EU)
• Summary of the legislation: Preventing abuse of the financial system for money laundering and terrorism purposes

Delegated and implementing acts

• Delegated and implementing acts to the AMLD IV

Transposition by EU Member States

• The AMLD IV was transposed by all EU Member states into their national law.
• Transposition history of the AMLD IV by EU Member States

Ongoing revision

• Ongoing revision of the AMLD IV
• Legislative initiative on the review of AMLD IV

Legislative history

• Original legislative proposal for the AMLD IV
• Impact assessment accompanying the legislative proposal for the AMLD IV
• Executive summary of the impact assessment accompanying the legislative proposal for the AMLD IV

Related links

FATF The Commission is a member of the Financial Action Task Force (FATF), the main international body concerned with combating money laundering, the financing of terrorism and other threats to the integrity of the international financial system.

MONEYVAL The Commission is an observer in Moneyval – the Council of Europe body assessing compliance with AML/CFT standards.

EGMONT The Commission is an observer at the Egmont Group, an international organisation that provides financial intelligence units with a platform for the secure exchange of expertise and financial intelligence to combat money laundering, terrorist financing, and associated predicate offences. 

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