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 progressed significantly, yet there remains a wide diversity in the conceptual frameworks used. The ClimHB conceptual framework, developed in 2019, integrates two influential models: the Levesque model of healthcare access and DFID's resilience framework. Designed to study health system resilience in response to climate-induced events, the ClimHB framework uniquely positions the population as an active participant on the demand side, complementing the supply side of health services and providers. Characterised by three core dimensions – exposure, sensitivity, and adaptive capacity – this dual focus on demand and supply, and their interactions emphasises the dynamic interplay between both sides in shaping health system resilience. A workshop utilising framework analysis, and the World Café method refined the ClimHB framework by integrating empirical evidence from Haiti and Bangladesh, alongside insights from a literature review. The revised framework presents a dynamic understanding of interrelated resilience, aimed at informing decision-making across all levels of healthcare. It emphasises the importance of contextual factors, strengthens outcome linkages, and incorporates socio-economic and ecological considerations. Governance, professional awareness, and supply-side feedback loops were also emphasised. Site studies demonstrated the framework’s adaptability and ability to foster synergy between theory and implementation. However, challenges persist in operationalising the framework, particularly for policymakers, emphasising the need for validation, standardised measures, and a deeper understanding of resilience interplays. Future research should explore the framework’s implications for structural management, 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 | 0.005 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.009 |
| 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".