Improving an integrative framework of health system resilience and climate change: Lessons from Bangladesh and Haiti
Bibliographic record
Abstract
The analysis of health system resilience has advanced considerably, yet a wide range of conceptual frameworks continues to be employed. The ClimHB conceptual framework, developed in 2019, combines two influential models: the Levesque model of healthcare access and the DFID’s resilience framework. It is designed to examine health system resilience in response to climate-induced events. What sets the ClimHB framework apart is its emphasis on the population as an active participant on the demand side, complementing the supply side represented by healthcare services and providers. The framework is defined by three key dimensions – exposure, sensitivity, adaptive capacity. Its dual focus on demand and supply highlights their dynamic interaction in shaping health system resilience. A workshop and the World Café method refined the ClimHB framework by incorporating empirical data from Haiti and Bangladesh with findings from a literature review. The updated framework offers a dynamic perspective on resilience, focusing on the interconnected nature of its elements to guide decision-making across all levels of health systems. Key enhancements include greater emphasis on contextual factors, highlighting the influence of socio-economic and ecological conditions. It also features strengthened connections between resilience outcomes and contextual variables, improving the understanding of how context affects results. Governance and professional awareness were highlighted as critical elements for improving health system responses, and feedback loops were integrated in the supply side to enhance adaptability and decision-making processes. Empirical studies have demonstrated the ClimHB framework’s adaptability and capacity to create synergy between theoretical concepts and practical implementation. However, challenges remain in operationalising the framework for policymakers. These challenges highlight the need for further validation of the framework, the development of standardised measures, and a deeper understanding of resilience dynamics. Future research should prioritise the framework’s implications for structural management, workforce training, and resource allocation, addressing critical gaps in resilience research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".