Improving access to emergency obstetric care in underserved rural Tanzania
Bibliographic record
Abstract
Objective To describe the results of improving availability of, and access to emergency obstetric care (EmOC) services in underserved rural Tanzania. Design Prospective cohort study Settings Rural Tanzania Methods Forty two associate clinicians from five health centres were trained in teams for three months in emergency obstetric care and anaesthesia. Two health centres were unexposed to the intervention and served as controls. Following training, virtual teleconsultation, quarterly on-site supportive supervision and continuous mentorship were implemented to reinforce skills and knowledge. Main outcome measures Proportion of all births in emergency obstetric care facilities, met need for EmOC and case fatality rate. Results The met need for EmOC increased significantly from 45% (459/1,025) at baseline (July 2014 – June 2016) to 119% (2,010/1,691) during the intervention period (Jul 2016 – June 2019). The met need for EmOC in the control group also increased from 53% (95% CI 49%-58%) to 77% (95% CI 74%-80%). Forty maternal deaths occurred during the baseline and intervention periods in the control and intervention health centres. The direct obstetric case fatality rate decreased slightly from 1.5% (95% CI 0.6%–3.1%) to 1.1% (95% CI 0.7%–1.6%) in the intervention group and from 3.3% (95% CI 1.2%–7.0%) to 0.8% (95% CI 0.2%–1.7%) in the control group. Conclusions When EmOC services are made available the proportion of obstetric complications treated in the facilities increases. However, the effort to scale up EmOC services in underserved rural areas should be accompanied by strategies to reinforce skills and the referral system.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".