Learning analysis of health system resilience

Bibliographic Data

ID16763160
AuthorsKyaw Myat Thu (0009-0002-3375-6931, The University of Sydney), Bernay (0000-0001-7628-8408, The University of Sydney), Seye Abimbola (0000-0003-1294-3850, The University of Sydney, corresponding author)
Year2025
Volume40
Issue3
Pages428-435
Publication date2025-03-07
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueHealth Policy and Planning (JOURNAL)
Journal identifiersISSN: 0268-1080 • E-ISSN: 1460-2237
PublisherOxford University Press (PUBLISHER • GB)
DOI10.1093/heapol/czae113
PMID39575662
OpenAlexW4404610255
LanguageEN
Citations received3
References cited32

The emergence of ‘resilience’ as a concept for analysing health systems—especially in low- and middle-income countries—has been trailed by debates on whether ‘resilience’ is a process or an outcome. This debate poses a methodological challenge. What ‘health system resilience’ is interpreted to mean shapes the approach taken to its analysis. To address this methodological challenge, we propose ‘learning’ as a concept versatile enough to navigate the ‘process versus outcome’ tension. Learning—defined as ‘the development of insights, knowledge, and associations between past actions, the effectiveness of those actions, and future actions’—we argue, can animate features that tend to be silenced in analyses of resilience. As with learning, the processes involved in resilience are cyclical: from absorption to adaptation, to transformation, and then to anticipation of future disruption. Learning illuminates how resilience occurs—or fails to occur—interactively and iteratively within complex systems while acknowledging the contextual, cognitive, and behavioural capabilities of individuals, teams, and organizations that contribute to a system’s emergence from or evolution given shocks/stress. Learning analysis can help to resist the pull towards framing resilience as an outcome—as resilience is commonly used to mean or suggest a state or an attribute, rather than a process that unfolds, whether the outcomes are deemed positive or not. Analysing resilience as a learning process can help health systems researchers better systematically make sense of health system responses to present and future stress/shocks. In qualitative or quantitative analyses, seeing what is to be analysed as ‘learning’ rather than the more nebulous ‘resilience’ can refocus attention on what is to be measured, explained, and how—premised on the understanding that a health system with the ability to learn is the one with the ability to be resilient, regardless of the outcome of such a process

Adaptation (eye) · Anticipation (artificial intelligence) · Cognitive psychology · Cognitive science · Economics · Framing (construction) · Outcome (game theory) · Process (computing) · Psychological resilience · Resilience (materials science) · Artificial Intelligence · Climate Change and Health Impacts · Computer Science · Disaster Response and Management · Engineering · Health and Conflict Studies · Psychology · Social Psychology