Community member and policy maker priorities in improving maternal health in rural Tanzania
Bibliographic record
Abstract
OBJECTIVE: To determine community member and policy maker priorities in improving maternal health in rural Tanzania. METHODS: The present participatory action research project was conducted in Rorya District, Mara Region, Tanzania, between November 20 and 25, 2015. A convenience sample of four community and one policy maker discussion groups were held to identify factors impacting on maternal health. The inclusion criterion for community members was a recent personal or partner experience with childbirth, or experience as a village leader. The policy maker participants were enrolled from all members of the District Council Health Management Team. RESULTS: There was considerable overlap in priorities expressed by community members and policy makers. The most common priorities were to improve the transportation options for women to get to the health facility, the availability of supplies in the health facilities, and healthcare provider attitudes toward women, and to increase the number of skilled healthcare providers. Policy makers also prioritized improved health education of women, improved access to health facilities, and increased power in decision-making for women. CONCLUSIONS: Community members and policy makers have similar priorities for improving maternal health, which involve both social and structural changes.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".