Learning analysis of health system resilience.

Abstract

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.