The contribution of community health systems to resilience: Case study of the response to the 2015 earthquake in Nepal
Bibliographic record
Abstract
Methods: Key informant interviews and focus group discussions were utilised. Participants included FCHVs, primary healthcare workers, community leaders and mothers, district health managers, representatives from the Ministry of Health and Population, multilateral health organisations, bilateral development partners, local non-governmental organisations, community-based organisations, and international non-governmental organisations. We used thematic content analysis to identify emerging themes. Results: Seventy-seven people participated in the study in September 2016 from communities (n = 53, 69%), districts (n = 8, 10%), and national levels (n = 16, 21%). Strong coordination, international and national support, and community engagement and participation were reported as successes of the earthquake response. Challenges included a lack of preparedness and supplies, a lack of earthquake-resistant infrastructure, and the centralisation of the response. FCHVs continued to work, despite being victims of the earthquake themselves. Facilitators of the continuation of the FCHVs' duties included their strong ties with the communities and facilities, international support, and the ability to mobilise existing community resources. Barriers included fear, communities' attitudes, high workloads, large geographic distances, and difficult geography. Participants identified the importance of having strong, connected, and supported communities, adaptable funding and policies, and decentralised decision-making within strong health systems. Conclusions: Building resilience in community-based health systems must start with strong communities that are prepared, trained, equipped, and empowered. Health systems must be decentralised and adaptable, with strong coordination and leadership. Capable community health workers such as FCHVs were an important part of building resilience during the earthquake. These lessons can assist countries in strengthening decentralised health systems to better respond to a multitude of shocks, while still providing essential health services for 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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".