Implementing community-based health program in conflict settings: documenting experiences from the Central African Republic and South Sudan
Bibliographic record
Abstract
Abstract Background The delivery of quality healthcare for women and children in conflict-affected settings remains a challenge that cannot be mitigated unless global health policymakers and implementers find an effective modality in these contexts. The International Committee of the Red Cross (ICRC) and the Canadian Red Cross (CRC) used an integrated public health approach to pilot a program for delivering community-based health services in the Central African Republic (CAR) and South Sudan in partnership with National Red Cross Societies in both countries. This study explored the feasibility, barriers, and strategies for context-specific agile programming in armed conflict affected settings. Methods A qualitative study design with key informant interviews and focus group discussions using purposive sampling was used for this study. Focus groups with community health workers/volunteers, community elders, men, women, and adolescents in the community and key informant interviews with program implementers were conducted in CAR and South Sudan. Data were analyzed by two independent researchers using a content analysis approach. Results In total, 15 focus groups and 16 key informant interviews were conducted, and a total of 169 people participated in the study. The feasibility of service delivery in armed conflict settings depends on well-defined and clear messaging, community inclusiveness and a localized plan for delivery of services. Security and knowledge gaps, including language barriers and gaps in literacy negatively impacted service delivery. Empowering women and adolescents and providing context-specific resources can mitigate some barriers. Community engagement, collaboration and negotiating safe passage, comprehensive delivery of services and continued training were key strategies identified for agile programming in conflict settings. Conclusion Using an integrative community-based approach to health service delivery in CAR and South Sudan is feasible for humanitarian organizations operating in conflict-affected areas. For agile, and responsive implementation of health services in conflict-affected settings, decision-makers should focus on effectively engaging communities, bridge inequities through the engagement of vulnerable groups, collaborate and negotiate for safe passage for delivery of services, keep logistical and resource constraints in consideration and contextualize service delivery with the support of local actors.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".