Examining the role of community health workers amid extreme weather events in low- and middle-income countries: a scoping review
Bibliographic record
Abstract
OBJECTIVES: The increased frequency and severity of extreme weather events (EWEs) have underscored the need to strengthen climate-resilient health systems and capacity. Community health workers (CHWs) are integral health systems actors with the potential to protect and improve population health in a changing climate. The aim of this review was to synthesize the literature on the roles of CHWs amid EWEs in low- and middle-income countries, the barriers and facilitators to implement these roles, and program supports to strengthen CHW capacity and health system functions. STUDY DESIGN: Scoping review. METHODS: Four academic databases and gray literature published between January 2000 and June 2023 were searched. Data were thematically analyzed using a deductive-inductive approach guided by the World Health Organization's (WHO's) Operational framework for building climate-resilient health systems. RESULTS: Thirty sources were included. Amid EWEs, CHW roles included: 1) delivery of diagnostic, treatment, and other clinical services; 2) support with access, utilization, or navigation of health services and/or referrals; 3) community education and health promotion; 4) data collection and health surveillance; 5) psychosocial supports; and 6) weather-related health emergency response. Facilitators and barriers to the provision of CHW supports amid EWEs were categorized within WHO's building blocks of health systems. Considerations for strengthening CHW programs to enhance climate-resilient health systems are also discussed. CONCLUSIONS: CHWs are uniquely positioned to provide health-related supports amid EWEs that extend to emergency preparedness and response to climate-health challenges. These efforts can contribute to the community and health systems resilience to climate change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".