Community engagement in health systems interventions and research in conflict-affected countries: a scoping review of approaches
Bibliographic record
Abstract
BACKGROUND: Healthcare research, planning, and delivery with minimal community engagement can result in financial wastage, failure to meet objectives, and frustration in the communities that programmes are designed to help. Engaging communities - individual service-users and user groups - in the planning, delivery, and assessment of healthcare initiatives from inception promotes transparency, accountability, and 'ownership'. Health systems affected by conflict must try to ensure that interventions engage communities and do not exacerbate existing problems. Engaging communities in interventions and research on conflict-affected health systems is essential to begin addressing effects on service delivery and access. OBJECTIVE: This review aimed to identify and interrogate the literature on community engagement in health system interventions and research in conflict-affected settings. METHODS: We conducted a scoping review using Arksey & O'Malley's framework, synthesising the data descriptively. RESULTS: We included 19 of 2,355 potential sources identified. Each discussed at least one aspect of community engagement, predominantly participatory methods, in 12 conflict-affected countries. Major lessons included the importance of engaging community and religious leaders, as well as people of lower socioeconomic status, in both designing and delivering culturally acceptable healthcare; mobilising community members and involving them in programme delivery to increase acceptability; mediating between governments, armed groups and other organisations to increase the ability of healthcare providers to remain in post; giving community members spaces for feedback on healthcare provision, to provide communities with evidence that programmes and initiatives are working. CONCLUSION: Community engagement in identifying and setting priorities, decision-making, implementing, and evaluating potential solutions helps people share their views and encourages a sense of ownership and increases the likely success of healthcare interventions. However, engaging communities can be particularly difficult in conflict-affected settings, where priorities may not be easy to identify, and many other factors, such as safety, power relations, and entrenched inequalities, must be considered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.086 | 0.168 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.035 | 0.040 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".