Community health worker contributions to climate resilient health systems: A qualitative study of how community health workers navigate extreme weather events in the Philippines
Bibliographic record
Abstract
Community health workers (CHWs) are an important source of health-related support and care across many resource-constrained settings. However, amid the increasing frequency and intensity of extreme weather events globally, there is a need to understand how CHWs may experience and navigate these events, and consider potential opportunities to strengthen their contributions to climate resilient health systems. This study explored the experiences of CHWs before, during, and after extreme weather events to examine how their identities, existing responsibilities, and perceived capacity shaped their ability to contribute towards preparedness, response, and recovery efforts in their communities. In collaboration with a Philippines-based non-governmental organization (NGO), we conducted 51 semi-structured interviews with CHWs affiliated with an NGO-led CHW program across four municipalities in Negros Oriental, Philippines. All interviews were audio-recorded, transcribed, and thematically analyzed using a hybrid deductive-inductive approach. Participants described how the various roles and responsibilities they held within and outside of their household shaped their engagement with preparedness, response, and recovery to extreme weather events. Importantly, participants highlighted opportunities to enhance their contributions to preparedness, response, and recovery efforts in their communities. Further, participants discussed how additional training and material resources could be leveraged to enhance preparedness to and recovery from extreme weather events in their communities. Overall, this study provides insight into how CHWs may contribute to climate resilient health systems amid extreme weather events, and underscores the complexities of recognizing CHWs as both health leaders and community members in disaster risk management practices. • Community health workers are a key source of support amid extreme weather events. • Community health workers demonstrated a motivation and capacity to provide support. • Community health workers are both health leaders and community members themselves. • Capacity to provide support was shaped by roles and responsibilities in communities.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".