Community-driven strategies for primary health care resilience in response to shocks in Latin America and the Caribbean: a scoping review and expert consultation
Bibliographic record
Abstract
Community engagement in Primary Health Care (PHC) enhances system resilience. This scoping review and expert consultation aimed to document the range of community-driven interventions and strategies implemented in Latin America and the Caribbean (LAC) in response to shocks and identify factors that enable or hinder their implementation. The research used a mixed-methods approach, including a scoping review of 70 studies from January 2019 to July 2024 and interviews with seven subject experts. The findings were then validated with six additional experts. The study identified 14 community-driven strategies, grouped into three main categories: community health worker participation, community engagement, and mobilization of civil society organizations. For each category, the researchers analyzed facilitators and challenges. This work provides a comprehensive compilation and analysis of community-driven interventions during emergencies in LAC. The findings offer valuable insights for improving emergency preparedness and response strategies in health systems.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".