Community-based transport system in Shinyanga, Tanzania: A local innovation averting delays to access health care for maternal emergencies
Bibliographic record
Abstract
In achieving the sustainable development goal 3.1, Tanzania needs substantial investment to address the three delays which responsible for most of maternal deaths. To this end, the government of Tanzania piloted a community-based emergency transport intervention to address the second delay through m-mama program. This study examined secondary data to determine the cost-effectiveness of this intervention in comparison to the standard ambulance system alone. The m-mama program was implemented in six councils of Shinyanga region. The m-mama program data analyzed included costs of referral services using the Emergency Transportation System (EmTS) compared with the standard ambulance system. Analysis was conducted using Microsoft Excel, whose data was fed into a TreeAge Pro Healthcare 2022 model. The cost and effectiveness data were discounted at 5% to make a fair comparison between the two systems. During m-mama program implementation a total of 989 referrals were completed. Of them, 30.1% used the standard referral system using ambulance, while 69.9% used the EmTS. The Emergency transport system costed USD 170.4 per a completed referral compared to USD 472 per one complete referral using ambulance system alone. The introduction of m-mama emergency transportation system is more cost effective compared to standard ambulance system alone in the context of Shinyanga region. Scaling up of similar intervention to other regions with similar context and burden of maternal mortality may save cost of otherwise normal emergency ambulance system. Through lessons learned while scaling up, the intervention may be improved and tailored to local challenges and further improve its effectiveness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".