Challenges and Successes of Distributing Birth Kits with Misoprostol to Reduce Maternal Mortality in Rural Tanzania.
Bibliographic record
Abstract
The Saving Mothers Project was conducted from September 2015 to March 2017 in Bunda and Tarime Districts, Mara Region, Tanzania. The purpose of this project was to train community health workers (CHWs) to use mobile phones applications to register and educate pregnant women about safe deliveries and encourage them to access skilled health care providers for antenatal care and delivery, and to provide nurses and CHWs with clean birth kits with misoprostol to distribute to women. The birth kits were for use in case women could not access the health facility, or if the health facility was lacking supplies at the time of delivery. The overall goal of the study was to reduce the maternal mortality rate by increasing women's access to health services where possible, and to clean supplies when a non-facility birth was unavoidable. This paper reports on a mixed methods evaluation of the project including a survey of over two thousand four hundred women, and focus groups with women, community health workers, and nurses participating in the project. The results of the survey and focus groups demonstrate a high degree of satisfaction with the birth kits and misoprostol and an increase in facility birth rates where the project was implemented. Differences between the two districts illustrate that policy maker support is key to successful implementation.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".