Community Participation in Primary Healthcare in the South Sudan Boma Health Initiative: A Document Analysis
Bibliographic record
Abstract
Community participation is central to primary healthcare, yet there is little evidence of how this works in conflict settings. In 2016, South Sudan's Ministry of Health launched the Boma Health Initiative (BHI) to improve primary care services through community participation.We conducted a document analysis to examine how well the BHI policy addressed community participation in its policy formulation. We reviewed other policy documents and published literature to provide background context and supplementary data. We used a deductive thematic analysis that followed Rifkin and colleagues' community participation framework to assess the BHI policy.The BHI planners included inputs from communities without details on how the needs assessment was conducted at the community level, what needs were considered, and from which community. The intended role of communities was to implement the policy under local leadership. There was no information on how the Initiative might strengthen or expand local women's leadership. Official documents did not contemplate local power relations or address gender imbalance. The policy approached households as consumers of health services.Although the BHI advocated community participation to generate awareness, increase acceptability, access to services and ownership, the policy document did not include community participation during policy cycle.
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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 | 0.025 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".