Measurement Instruments Assessing Organizational Resilience in Health Facilities: A Systematic Review of Psychometric Properties

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Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Measurement Instruments Assessing Organizational Resilience in Health Facilities: A Systematic Review of Psychometric Properties View ORCID Profile Atena Pasha , Kaylin Morris , Jannatun Nayem , Karolina Kaczmarski , Xiaoming Li , Shan Qiao doi: https://doi.org/10.1101/2025.06.11.25329057 Atena Pasha 1 Department of Health Promotion, Education and Behavior, Arnold School of Public Health, University of South Carolina , Columbia, SC, USA 2 South Carolina SmartState Center for Healthcare Quality, Arnold School of Public Health, University of South Carolina , Columbia, SC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Atena Pasha Kaylin Morris 3 South Carolina Honors College, University of South Carolina , Columbia, SC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jannatun Nayem 1 Department of Health Promotion, Education and Behavior, Arnold School of Public Health, University of South Carolina , Columbia, SC, USA 2 South Carolina SmartState Center for Healthcare Quality, Arnold School of Public Health, University of South Carolina , Columbia, SC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Karolina Kaczmarski 3 South Carolina Honors College, University of South Carolina , Columbia, SC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Xiaoming Li 1 Department of Health Promotion, Education and Behavior, Arnold School of Public Health, University of South Carolina , Columbia, SC, USA 2 South Carolina SmartState Center for Healthcare Quality, Arnold School of Public Health, University of South Carolina , Columbia, SC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Shan Qiao 1 Department of Health Promotion, Education and Behavior, Arnold School of Public Health, University of South Carolina , Columbia, SC, USA 2 South Carolina SmartState Center for Healthcare Quality, Arnold School of Public Health, University of South Carolina , Columbia, SC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: shanqiao{at}mailbox.sc.edu Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Background: The COVID-19 pandemic has underscored the critical importance of resilient health systems. Organizational resilience, as a multidimensional construct encompassing cognitive, behavioral, and contextual capacities, is essential for ensuring continuity of care during crises. However, the availability of psychometrically sound instruments for assessing organizational resilience within healthcare facilities remains limited. Methods: Following PRISMA guidelines, a comprehensive systematic review was conducted across PubMed, Embase, PsycINFO, CINAHL, and Web of Science to identify empirical studies evaluating the psychometric properties of organizational resilience instruments applied in health facilities. The psychometric properties and methodological quality of the identified instruments were appraised using the COSMIN checklist. The review was pre-registered in PROSPERO (ID: CRD42024511040). Results: Of the 7,479 records screened, 29 studies were included evaluating 23 distinct organizational resilience instruments. Internal consistency was reported in 21 studies, test-retest reliability in 6 studies, content validity in 24 studies, structural validity in 21 studies, criterion validity in 15 studies, and construct validity in 24 studies. The Nurse Team Resilience Scale emerged as the most psychometrically robust tool. Conclusions: This review provides a critical synthesis of the available instruments for measuring organizational resilience in health facilities, revealing significant psychometric and methodological gaps. There is an urgent need to develop and validate context-specific, system-level resilience instruments incorporating intersectionality and cultural sensitivity. Introduction The COVID-19 pandemic has profoundly hit the global healthcare system with significant challenges and disruptions in healthcare delivery. Health organizations have had to quickly adapt by reallocating and reorganizing resources to meet the needs of COVID-19 patients, amidst high demand for urgent healthcare and sustained periods of increased activity ( World Health Organization, 2022 ; Pasha et al., 2025 ; Moynihan et al., 2020 ). During the pandemic, health facilities focused on adapting public health emergencies to provide patient care, address employee health concerns, and support employee psychological safety and mental health. However, many health facilities have struggled to demonstrate sufficient resilience to effectively cope with the crisis ( Kaczmarski et al., 2024 ). Organizational resilience refers to an organization’s ability to maintain functions and recover swiftly from adversity by mobilizing the necessary resources (Hillmann & Guenther, 2021). In the context of health facilities, it encompasses how hospitals and healthcare clinics monitor and plan for fluctuations in demand, handle shocks or stressors, and adapt and learn (Gröschke et al., 2022). Improving the organizational resilience of health systems can reduce vulnerability to crises, better preparing them to respond effectively while minimizing disruption to essential healthcare services (Kruk et al., 2015; Kruk et al., 2017; Kieny et al., 2014; Kaczmarski et al., 2024 ; Blanchet et al., 2017). Policymakers and researchers increasingly rely on academic literature to comprehend how to build resilience in healthcare organizations and measure this concept ( Ignatowicz et al., 2023 ). Consequently, there is a growing need to develop methods or metrics to measure and monitor organizational resilience in healthcare. Despite the heightened interest in the concept of resilience and its practical applications, the number of studies focusing on organizational resilience is still small in terms of establishing a valid measurement scale for organizational resilience ( Kaczmarski et al., 2024 ; Mallak, 1998; Pal et al., 2014; Richtnér & Löfsten, 2014; e.g., Barasa et al., 2018), with even fewer studies on developing resilience measurement frameworks and indices for the healthcare context. Measuring organizational resilience in healthcare facilities poses challenges due to the scarcity of validated indicators and measurement tools. Several models and metrics have been developed, but no universally accepted approach exists for measuring or assessing organizational resilience. The existing research has mostly focused on specific aspects of organizational resilience in relation to other variables instead of looking at organizational resilience as a whole (Gröschke et al., 2022). They either measure a set of characteristics such as knowledge, learning, planning, agility, adaptability, and robustness (e.g., Jeffcott et al., 2009; Wreathall’s, 2006) or evaluate capacity to deal with shocks and stresses (e.g., Ambulkar et al., 2015 ; Brandon Jones et al., 2014 ). However, their applicability to a healthcare context may be limited. It is imperative to note that the currently existing reviews have not yet comprehensively evaluated the precise psychometric properties of the instruments encompassing both organizational resilience and healthcare facilities. It is also noteworthy that, except for one review ( Ignatowicz et al., 2023 ), none of the other reviews narrowed their inclusion criteria to studies that exclusively assessed at least one psychometric property. Although Ignatowicz et al. (2023) offered an important synthesis of resilience assessment approaches in healthcare, their review did not systematically appraise the psychometric rigor or methodological quality of the identified instruments. Therefore, it is crucial to develop robust and conceptually sound approaches for assessing organizational resilience in order to ensure that the measures are reliable, robust, and truly reflect the realities faced by healthcare facilities. The psychometric properties of reliability and validity are fundamental aspects that need to be considered when assessing the effectiveness of an instrument ( DeVellis & Thorpe, 2021 ). Reliability measures the consistency of results over time and space, and there are several metrics used to assess reliability, including Cronbach’s alpha and test-retest reliability ( Boateng et al., 2018 ; DeVellis & Thorpe, 2021 ). Cronbach’s alpha assesses the internal consistency of the scale ( Cronbach, 1951 ), while test-retest reliability evaluates the stability of the instrument’s results over time ( DeVellis & Thorpe, 2021 ). On the other hand, validity addresses whether the instrument accurately measures the intended construct ( Davis, 2020 ). It is essential to consider various types of validity, such as content validity, structural validity, construct validity, and criterion validity. Therefore, the aim of this study was to bridge the knowledge gap and enhance our understanding of the psychometric properties of measurement instruments for organizational resilience in health facilities. Through a comprehensive systematic review, we evaluated the psychometric properties of various instruments and identified the most robust options. The methodological framework was primarily informed by the measurement properties outlined by Terwee et al. (1989; 2012 ). Their comprehensive approach provided quality criteria for various psychometric properties: (1) content validity, (2) internal consistency (Cronbach’s alpha), (3) criterion validity, (4) construct validity, (5) reproducibility (test-retest reliability), (6) structural validity, (7) responsiveness, and (8) interpretability. We focused on evaluating all except responsiveness and interpretability. Responsiveness detects clinically important changes over time ( Guyatt et al., 1989 ). Interpretability identifies whether the score difference between groups is meaningful ( Lohr et al., 1996 ). Our emphasis was more on establishing the fundamental measurement properties, i.e., reliability and validity, at a single point in time for a single group to ensure a robust foundation for any future psychometric assessments with longitudinal data and multiple groups ( DeVellis & Thorpe, 2021 ). Methods Search Strategies and Data Sources The protocol for this systematic review follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines ( Moher et al., 2015 ). The authors, KM and KK, conducted a comprehensive search from March to May 2024 across five bibliographic databases, namely PubMed, Embase, PsycINFO, CINAHL, and Web of Science (see Appendix 1 ). The search strategy included keywords from three categories. The first category comprised keywords related to health facilities, such as “Health Facilities,” “Health Services,” and “Health Delivery.” The second category was related to organizational resilience and included keywords such as “organizational resilience,” “Resilience,” and “Resiliency.” The third category included keywords related to measurement, such as “Measure,” “Surveys,” and “Questionnaires.” Keywords were organized using the following logical operators: (1) keywords within one category were lined using the OR operator (e.g., Healthcare worker OR Health System), and (2) keywords across different categories were connected using the AND operator (e.g., Health Service AND organizational resilience AND measure) (see Appendix 1 ). Eligibility Criteria The criteria for inclusion in this study were two-fold: (1) the article must be published in English in a peer-reviewed journal, and (2) the study must evaluate at least one psychometric property of an instrument that measures the organizational resilience of health facilities. Excluded papers were those that claimed to measure organizational resilience without utilizing a corresponding organizational resilience scale. Additionally, papers not published in English were omitted from the review. Included studies were screened based on the eligibility criteria using the data management software RAYYAN ( Ouzzani et al., 2016 ). The study selection process consisted of three phases. First, all the duplicate studies were removed. Second, researchers (AP, KM, KK) screened all titles and abstracts. Studies that did not comprise an instrument assessing organizational resilience among health facilities were removed. Third, two researchers (KM, JN) conducted full-text screening of the remaining articles and removed studies that did not assess at least one psychometric property or collected only qualitative data. Finally, articles that met the inclusion criteria were selected for this review (see Figure 1 ). Any discrepancies were resolved through discussion (KM, JN). In cases where the agreement could not be reached, a third researcher (AP) was involved to adjudicate. Download figure Open in new tab Figure 1. Flow diagram of the literature search and articles selection (adapted from PRISMA 2020 guidelines for systematic reviews) ( Page et al., 2021 ) Data Extraction Key information on three main categories was extracted from each included study, including study features, psychometric properties of the instrument, and the methodological quality of the study. In terms of study features, data were extracted based on the name of the instrument developed and/or validated, the description of the instrument (factors, items, and response scale), population, sample size, and country of data collection. As for the psychometric properties of the instrument, data were extracted on internal consistency, test-retest reliability, content validity, structural validity, criterion validity, and construct validity. Information on study design and methodology was evaluated and rated for methodological quality assessment. Any discrepancies among data extractors (JN, KM) were resolved through group discussions. In cases where the agreement could not be reached, a third researcher (AP) was involved to adjudicate. This multi-stage review process ensured that all eligible studies were systematically identified and included in the final analysis. Psychometric Properties Assessment Psychometric properties of instruments were appraised using the established set of quality criteria for studies on the development and evaluation of health quality questionnaires ( Terwee et al., 2007 ; Terwee et al., 2012 ; Mokkink et al., 2010 ). These criteria encompassed distinct measurement properties relevant to our study, including reliability [internal consistency and test-retest] and validity [content, structural (EFA or CFA), construct, and criterion]. Each psychometric property was scored using a three-point rating scale: positive (+), indeterminate (?), and negative (−). Appendix 2 defines each property and the corresponding criteria. A “+” score was given for internal consistency with Cronbach’s alpha > 0.70, test-retest reliability with ICC/kappa ≥ 0.70 or significant Pearson’s correlation, content validity when literature review, expert consultation, and target population were involved, structural validity if the factors explained ≥ 50% of total variance, criterion validity if 50% of the correlations with gold standards were significant, and construct validity if ≥ 75% of the associations matched hypotheses. Failing these criteria, properties received a “−”. Psychometric properties were assigned “?” when indeterminable. Methodological Quality Assessment The quality of the measurement instruments was assessed using the COSMIN (Consensus-based Standards for the selection of health status Measurement Instruments) checklist ( Terwee et al., 2012 ; Mokkink et al., 2010 ). This checklist is widely used to evaluate the methodological quality of studies assessing psychometric properties, particularly in patient-reported outcome measures (PROMs). COSMIN is grounded in Classical Test Theory, which assumes that an observed score comprises a true score and random error, and offers a systematic approach for evaluating reliability, validity, and responsiveness of measurement instruments ( Cappelleri et al., 2014 ). Given that the COSMIN checklist is primarily designed for PROMs, its application to tools assessing organizational or system-level resilience (e.g., facility or healthcare system resilience tools) required adaptation. As no established methodological quality checklist exists specifically for organizational-level measures relevant to our review, the COSMIN checklist was used for all included studies regardless of the level of measurement (individual, team, organizational, or system). Where necessary, the items were interpreted and applied considering the organizational context, particularly in domains such as content validity, structural validity, and reliability assessments, to ensure relevance and applicability for facility-focused instruments. This approach is consistent with prior psychometric reviews in emerging fields where existing methodological standards have been adapted pragmatically ( Kupeli et al., 2019 ). The COSMIN checklist ( Mokkink et al., 2010 ; Terwee et al., 2012 ) comprises nine boxes, each evaluating the methodological quality of a specific psychometric property. The number of items in these boxes ranges from 5 to 18. Each item within a box is rated as “yes”, “not applicable (NA)”, or “not reported (NR)”. One item, “Were there any important flaws in the methods or design of the study?” has been removed due to subjectivity. Additionally, the item, “Was the sample size adequate?”, has been modified to whether an appropriate analysis was conducted pre-study to identify the adequate sample size, such as a power analysis. Two steps were followed to complete the COSMIN checklist. First, the measurement properties to be evaluated were determined. Second, each item in the corresponding box for a psychometric property was rated. Ratings were coded as 1 for “yes” and 0 for “NR”. An average score was calculated for all applicable items in a box, and the scores were scaled from 1 to 10. Higher scores indicated better methodological quality. Ratings of 7.1-10 were considered “strong,” 3.1-7 as “moderate,” and 1-3 as “limited” in terms of methodological quality. The assessment of psychometric properties and methodological quality was conducted by two independent reviewers (JN, KM). Any discrepancies in ratings were discussed and resolved among the reviewers. In cases where the agreement could not be reached, a third researcher (AP) was involved to adjudicate. The involvement of multiple independent reviewers and resolving discrepancies likely enhances the reliability and consistency of the assessment process using the COSMIN checklist. Data Synthesis An additional scoring system was developed to facilitate comparison across all instruments on their psychometric and methodological quality ( Kupeli et al., 2019 ). This scoring system involved assigning scores to the psychometric and methodological quality of each assessed property (as illustrated in Appendix 3 ). Properties with “+” rating on psychometric quality and “strong,” “moderate,” or “limited” ratings on methodological quality were scored 3, 2, or 1, respectively; and properties with “−” rating on psychometric quality and “strong,” “moderate,” or “limited” ratings on methodological quality were scored −1, −2, or −3, respectively. Higher scores indicated better psychometric and methodological quality. The assigned scores for each psychometric property were then aggregated to calculate an overall score for each instrument. This approach allowed for a quantitative comparison of the instruments based on their combined psychometric and methodological performance. Results Characteristics of the Reviewed Studies The characteristics of the reviewed studies are summarized in Table 1 . Of the 7,479 records initially identified, 29 studies were ultimately included in this systematic review. These studies evaluated the psychometric properties of 23 distinct instruments designed to measure resilience within health facility contexts. While our review focused on instruments assessing organizational resilience, we also identified and included instruments assessing resilience at the individual level due to their use in health facility settings and the absence of fully developed system-level tools. View this table: View inline View popup Table 1. Characteristics of Included Studies The instruments included in this review reflect diverse conceptualizations of resilience, spanning individual capacities (e.g., adaptability and social support) (e.g., Abiola & Udofia, 2011 ; Zhao et al., 2022 ) and organizational capabilities (e.g., preparedness, monitoring, learning, risk management) (e.g., Cay et al., 2021 ; Shirali et al., 2016; Li et al., 2019). At the individual level, resilience was conceptualized as a psychological or behavioral trait enabling staff to adapt and recover from workplace stress (e.g., Mealer et al., 2016 ; Lauridsen et al., 2017). At the organizational or system level, resilience referred to institutional capacity to maintain functionality, adapt during disruptions, and ensure continuity of care (e.g., Lo Sardo et al., 2019 ; Li et al., 2020). The reviewed instruments encompassed a variety of constructs, such as cognitive flexibility, team cohesion, leadership adaptability, culture, system preparedness, and organizational learning. For instance, the International Consortium for Organizational Resilience (ICOR) scale ( Cay et al., 2021 ) included components like leadership and preparedness, while the Nurse Team Resilience Scale (NTRS) ( Su et al., 2023 ) emphasized intra-team dynamics. Meanwhile, the Resilience Assessment Grid (RAG) (Safi et al., 2022) and the Resilience Engineering (RE) framework (Shirali et al., 2016) focused on institutional and systemic resilience performance. Studies were geographically diverse, spanning countries such as China ( Zhao et al., 2022 ; Huang & Yu, 2023; Chuang et al., 2020 ; He et al., 2021; Li et al., 2019; Li et al., 2020; Su et al., 2023 ; Zhong et al., 2014; Zhong et al., 2015), Iran (Akbari et al., 2024; Shirali et al., 2016; Rahmani et al., 2023), Japan ( Miyazaki et al., 2023 ), South Korea (Park et al., 2019), Denmark ( Lauridsen et al., 2017; Safi et al., 2022), United Kingdom (Pangallo et al., 2016), Italy (Capone & Petrillo 2015), United States of America ( Mealer et al., 2016 ), Indonesia ( Sutriningsih, 2023 ; Mojtahedi et al., 2021), Austria ( Lo Sardo et al., 2019 ), Philippines ( Cay et al., 2021 ), Saudi Arabia ( Al-Ayed, 2019 ), Greece ( Galanis et al., 2023 ), Nigeria ( Abiola & Udofia, 2011 ), Australia (Safi et al., 2024; Mann et al., 2021), South Sudan ( Odhiambo et al., 2020 ). Participants ranged from frontline healthcare workers and nurses to administrators and system-level decision-makers. Sample sizes varied widely from studies conducted within a single hospital setting (e.g., Chuang et al., 2020 ; Rahmani et al., 2023) to large-scale validations involving over 7,000 respondents ( Zhao et al., 2022 ; Odhiambo et al., 2020 ). Psychometric Properties and Their Methodological Quality of Instruments A summary of the psychometric properties of instruments is presented in Table 2 , and a summary of the methodological quality assessment is presented in Table 3 . Based on the scoring system ( Appendix 3 ), synthesized psychometric and methodological quality assessment results are presented in Table 4 . View this table: View inline View popup Table 2. Summary of psychometric properties View this table: View inline View popup Download powerpoint Table 3. Methodological quality assessment for the psychometric properties View this table: View inline View popup Table 4. Synthesis of quality of psychometric properties and methodological quality Internal consistency was reported as acceptable in 19 studies, and moderate in 2 studies, while 8 studies did not report internal consistency. Of the 29 studies, most commonly using Cronbach’s alpha, McDonald’s omega, or composite reliability. Strong reliability was observed for the ICOR (α = 0.965; Cay et al., 2021 ), NTRS (α = 0.944; Su et al., 2023 ), 14-item version Resilience Scale (RS-14) (α = 0.939 and α = 0.87; Zhao et al., 2022 ; Abiola & Udofia, 2011 ), and Resilience at Work (RAW) Scale (α = 0.903; Akbari et al., 2024). The Short Grit Scale (Grit-S) was α = 0.751 (He et al., 2021), while the Connor-Davidson Resilience Scale (CD-RISC-10) ranged from α = 0.87 to 0.924 (Lauridsen et al., 2017; Mealer et al., 2016 ; Galanis et al., 2023 ). Test-retest reliability was reported in six studies. The Grit-S scale demonstrated strong test-retest reliability, with a Pearson’s correlation coefficient of 0.885 (He et al., 2022). The RAW Scale demonstrated excellent test-retest reliability, with intraclass correlation coefficients of ≥ 0.75 (Akbari et al., 2024), and the SDMHR instrument demonstrated strong test-retest reliability, with Pearson’s correlation coefficient exceeding 0.80 (Li et al., 2019) and Resilience Engineering (RE) with a correlation coefficient of 0.95 (Shirali et al., 2016). Content validity was confirmed in 24 instruments via expert reviews, Delphi methods, or theoretical alignment. For instance, the NTRS reached a scale content validity index of 0.99 after expert revisions ( Su et al., 2023 ). Strong evidence was also found for RE (Shirali et al., 2016) and RAW (Akbari et al., 2024). Structural validity was evaluated in 21 studies using EFA or CFA. The Greek CD-RISC-10 had a one-factor model with excellent CFA fit ( Galanis et al., 2023 ); RAW achieved RMSEA = 0.06, CFI = 0.93 (Akbari et al., 2024); and NTRS had a one-factor solution explaining 72.3% variance ( Su et al., 2023 ). Sutriningsih (2023) and Mealer et al. (2016) also demonstrated good model fit using CFA. Criterion validity was assessed in 15 studies. The Danish CD-RISC-10 correlated with the Perceived Stress Scale (PSS) (r = −0.63; Lauridsen et al., 2017), the Model Dynamic scale correlated with resilience scores and physician density (r = −0.48; Lo Sardo et al., 2019 ). The Health System Resilience (HSR) demonstrated validity using known-group comparison ( Odhiambo et al., 2020 ). Construct validity was reported in 24 studies, supported by theoretical hypotheses or factor analysis. Examples include Strategic human resource management (SHRM) ( Al-Ayed, 2019 ), RAW (Akbari et al., 2024), and Grit-S ( Zhao et al., 2022 ). The NTRS ( Su et al., 2023 ) reported the average variance extracted (AVE) of 0.679 and the composite reliability of 0.944, confirming the good construct validity of the scale. Methodological quality (MQ) was rated using COSMIN standards (As shown in Table 3 ), with 77% of instruments achieving “strong” MQ scores in at least one psychometric domain. The NTRS ( Su et al., 2023 ) scored the highest overall (9), followed by a group of instruments scoring 8, including the SHRM ( Al-Ayed. 2019 ), SDMH (Li et al., 2019), SD-HFE (Li et al., 2020), and the Danish version of the CD-RISC-10 (Lauridsen et al., 2017), Resilience Scale for Nurses (Park et al., 2019), Human Resource Carrying Capacity Model (Huang & Yu, 2023), RS-14 ( Zhao et al., 2022 ), SJT (Pangallo et al., 2016), and Grit-S (He et al., 2021). In contrast, tools such as R@W Leader Scale (Mann et al., 2021) and the Hospital Disaster Resilience (Zhong et al., 2015) scored 3 and 4, reflecting significant limitations in reporting and methodological rigor, particularly in the areas of structural and criterion validity. Comparative Evaluation of Instruments Overall, based on combined psychometric ratings and methodological quality scores, the NTRS was the most psychometrically robust instrument, achieving a combined score of 9. This instrument demonstrated strong internal consistency, construct validity, and structural validity. A cluster of instruments scored 8, indicating strong psychometric and methodological quality, including the SHRM, SDMH, SD-HFE, and the Danish version of the CD-RISC-10, Resilience Scale for Nurses, Human Resource Carrying Capacity Model, RS-14, SJT, and Grit-S. In contrast, tools such as the R@W Leader Scale and the Hospital Disaster Resilience scored 3 and 4, reflecting significant limitations in reporting and methodological rigor, particularly in the areas of structural and criterion validity. These lower scores suggest caution in the interpretation and use of these instruments for comprehensive organizational resilience assessments. Instruments that achieved higher scores generally were grounded in conceptual frameworks and reported a greater number of psychometric properties with positive ratings and stronger methodological quality, underscoring their applicability in diverse health facility settings. Discussion This systematic review critically evaluated and synthesized the psychometric properties and methodological quality of instruments assessing organizational resilience in health facilities. Our findings expand the scope from focusing exclusively on healthcare workers to encompass facility and system-level instruments, uncovering a wider diversity of conceptual approaches, psychometric rigor, and gaps in the field. The NTRS ( Su et al., 2023 ) emerged as the most psychometrically robust tool, followed by a cluster of instruments scoring 8, including the SHRM ( Al-Ayed. 2019 ), SDMHR (Li et al., 2019), SD-HFE (Li et al., 2020), and the Danish version of the CD-RISC-10 (Lauridsen et al., 2017), Resilience Scale for Nurses (Park et al., 2019), Human Resource Carrying Capacity Model (Huang & Yu, 2023), RS-14 ( Zhao et al., 2022 ), SJT (Pangallo et al., 2016), and Grit-S (He et al., 2021). These instruments demonstrated strong internal consistency, construct validity, and acceptable structural validity. However, none of them provided comprehensive reporting on test-retest reliability or criterion validity, which weakens the ability to assess their longitudinal stability and predictive performance ( Guyatt et al., 1989 ; DeVellis & Thorpe, 2021 ). Consistent with prior research ( Kupeli et al., 2019 ; Boateng et al., 2018 ), the findings of this review highlight that while internal consistency remains the most frequently reported psychometric property, other essential measurement properties, such as test-retest reliability and criterion validity, are still underreported or insufficiently assessed. Approximately 77% of the instruments achieved a strong methodological quality rating in at least one domain; however, few demonstrated comprehensive psychometric assessment across all domains, confirming gaps observed in prior systematic reviews of health measures ( Mokkink et al., 2010 ; Terwee et al., 2012 ). Cultural adaptation of instruments emerged as a notable area of progress, particularly the cross-validation of the CD-RISC-10 and RS-14 in diverse contexts such as Denmark, Greece, the Philippines, China, and Nigeria. While these adaptations generally demonstrated acceptable internal consistency and construct validity, the lack of rigorous cross-cultural validation or measurement invariance testing remains a concern ( Chen, 2008 ; Cappelleri et al., 2014 ). Despite the increasing attention to resilience measurement, only a subset of instruments (n=11) explicitly targeted organizational or system-level constructs, such as the RE framework ( Ghadirian et al., 2022 ), the RAG (Hollnagel, 2011), and SDMHR (Li et al., 2019). These tools, although conceptually aligned with system resilience frameworks ( Lengnick-Hall et al., 2011 ; WHO, 2022), lacked full psychometric evaluations, particularly in structural validity, criterion validity, and test-retest reliability, limiting their practical applicability in comparative health systems research. In terms of methodological challenges, aligned with prior reviews ( Mokkink et al., 2010 ; Lohr et al., 1996 ), this review found several methodological challenges, such as incomplete reporting of sample characteristics, lack of hypothesis testing in construct validity assessments, and absence of measurement error reporting. For example, several studies reported internal consistency without conducting factor analyses or failed to report factor loadings, undermining the rigor of their construct validation ( Terwee et al., 2007 ; Cronbach, 1951 ). Moreover, the COSMIN checklist underscored the methodological interdependence between structural validity and internal consistency, as factor analysis should precede reliability testing to ensure the coherence of scale items ( Mokkink et al., 2010 ; Terwee et al., 2012 ). Despite this, several instruments reported alpha coefficients without supporting factor structures, highlighting concerns raised in psychometric best practices ( Boateng et al., 2018 ; DeVellis & Thorpe, 2021 ). Consistent with Ignatowicz et al. (2023) , this review identified the absence of intersectional approaches in resilience measurement. Resilience, as a multifaceted and context-dependent phenomenon, intersects with multiple structural determinants including gender, race, socio-economic status, and organizational power dynamics ( Gkiouleka et al., 2018 ). Future research should integrate intersectional and systems-thinking approaches into the development and validation of resilience measures to capture the complex realities of diverse healthcare workforces and facilities ( Sturgess, 2016 ). Integrative frameworks are needed to bridge this gap, ensuring resilience is measured across nested levels of influence, from frontline care delivery to systemic preparedness and adaptability. Limitations This review has several limitations. First, while two researchers conducted independent data extraction, intercoder reliability was not formally reported. Second, the review was limited to peer-reviewed articles published in English and indexed in selected databases, potentially excluding relevant gray literature, dissertations, and non-English studies. Third, while the review assessed six core psychometric properties, measurement invariance and cross-cultural validation were not evaluated systematically, given the focus on instrument validation at a single point in time. Fourth, although the COSMIN checklist is widely used, it is based on classical test theory and for PROMs and may not fully capture alternative modeling approaches such as item response theory or generalizability theory, and the quality of measurement instruments assessing structural level resilience. Finally, the scoring system weighed all psychometric properties equally, which may not reflect their contextual relevance in different research or practice settings. Moreover, instruments reporting fewer properties but with higher-quality ratings may appear more robust than those reporting more properties but receiving mixed ratings, necessitating cautious interpretation of the scoring. Future Directions Future research should prioritize the development and rigorous psychometric validation of organizational resilience instruments, ensuring they capture both structural and contextual determinants of resilience in healthcare settings. There is also a need for longitudinal validation to assess the temporal stability of these instruments and their predictive validity in relation to organizational outcomes. Finally, researchers are encouraged to report their methods transparently, following the COSMIN and PRISMA guidelines, and engage in cross-cultural validation using advanced psychometric techniques such as measurement invariance testing and item response theory models to improve the global applicability and comparability of organizational resilience instruments. Notably, this review found no instruments specifically designed to evaluate the quality of structural-level organizational resilience. Existing evaluation frameworks, such as COSMIN, are tailored for patient-reported outcomes and may not adequately assess system-level constructs. Future tool development should therefore prioritize frameworks that explicitly capture the structural and operational dynamics of healthcare resilience. Conclusion This comprehensive systematic review critically appraised the psychometric properties and methodological quality of instruments designed to measure organizational resilience among health facilities. Based on combined psychometric ratings and methodological quality scores, the NTRS emerged as the most psychometrically robust instrument in this review. The overall evaluation revealed that while an increasing number of instruments are being developed and adapted, only a limited subset has undergone comprehensive psychometric evaluation, particularly at the organizational and system levels. Our findings confirm the urgent need for the development and validation of psychometrically robust, culturally sensitive, and context-specific organizational resilience instruments. Additionally, a comprehensive psychometric evaluation, including test-retest reliability, criterion validity, and measurement invariance testing, remains essential to strengthen the evidence base for these instruments. Given the limited availability of system-level resilience measures with adequate psychometric support, future research should also prioritize the development of new tools that comprehensively address organizational resilience within diverse health settings. Data Availability All data produced in the present work are contained in the manuscript. Contribution Atena Pasha (Investigation, Methodology, Project administration, Resources, Formal analysis, Writing – original draft, Writing – review and editing), Kaylin Morris, Jannatun Nayem, Karolina Kaczmarski (Investigation, Data curation, Formal analysis), Xiaoming Li (Conceptualization, Supervision, Writing – review and editing), Shan Qiao (Conceptualization, Supervision, Methodology, Writing – review and editing). Funding The study was funded by NIH/NIAID (Grant #R01AI174892). Acknowledgements Declaration of competing interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References ↵ Abiola , T. , & Udofia , O . ( 2011 ). Psychometric assessment of the Wagnild and Young’s resilience scale in Kano, Nigeria . BMC Research Notes , 4 , 1 – 5 . OpenUrl CrossRef PubMed ↵ Al-Ayed , S. I . ( 2019 ). 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Share Measurement Instruments Assessing Organizational Resilience in Health Facilities: A Systematic Review of Psychometric Properties Atena Pasha , Kaylin Morris , Jannatun Nayem , Karolina Kaczmarski , Xiaoming Li , Shan Qiao medRxiv 2025.06.11.25329057; doi: https://doi.org/10.1101/2025.06.11.25329057 Share This Article: Copy Citation Tools Measurement Instruments Assessing Organizational Resilience in Health Facilities: A Systematic Review of Psychometric Properties Atena Pasha , Kaylin Morris , Jannatun Nayem , Karolina Kaczmarski , Xiaoming Li , Shan Qiao medRxiv 2025.06.11.25329057; doi: https://doi.org/10.1101/2025.06.11.25329057 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Health Systems and Quality Improvement Subject Areas All Articles Addiction Medicine (566) Allergy and Immunology (863) Anesthesia (297) Cardiovascular Medicine (4411) Dentistry and Oral Medicine (443) Dermatology (380) Emergency Medicine (606) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1505) Epidemiology (15205) Forensic Medicine (30) Gastroenterology (1119) Genetic and Genomic Medicine (6575) Geriatric Medicine (666) Health Economics (994) Health Informatics (4511) Health Policy (1365) Health Systems and Quality Improvement (1608) Hematology (537) HIV/AIDS (1263) Infectious Diseases (except HIV/AIDS) (15903) Intensive Care and Critical Care Medicine (1103) Medical Education (620) Medical Ethics (144) Nephrology (666) Neurology (6573) Nursing (345) Nutrition (998) Obstetrics and Gynecology (1139) Occupational and Environmental Health (954) Oncology (3319) Ophthalmology (968) Orthopedics (369) Otolaryngology (420) Pain Medicine (435) Palliative Medicine (129) Pathology (662) Pediatrics (1689) Pharmacology and Therapeutics (691) Primary Care Research (710) Psychiatry and Clinical Psychology (5423) Public and Global Health (9205) Radiology and Imaging (2191) Rehabilitation Medicine and Physical Therapy (1367) Respiratory Medicine (1191) Rheumatology (593) Sexual and Reproductive Health (709) Sports Medicine (529) Surgery (709) Toxicology (99) Transplantation (288) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9fec27869e5e06db',t:'MTc3OTI4ODYzMw=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();