Back

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

Pasha, A.; Morris, K.; Nayem, J.; Kaczmarski, K.; Li, X.; Qiao, S.

2025-06-11 health systems and quality improvement
10.1101/2025.06.11.25329057 medRxiv
Show 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.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.