Lessons learnt and best practices in scaling up an emergency transportation system to tackle maternal and neonatal mortality: a qualitative study of key stakeholders in Shinyanga, Tanzania
Bibliographic record
Abstract
OBJECTIVE: This study aimed to document lessons learnt and best practices for scaling up an innovative emergency transportation system, drawing insights from the m-mama programme implemented in Shinyanga, Tanzania. The m-mama pilot programme was implemented in phases from 2014 to 2016 in two districts and later scaled up to include all districts in Shinyanga region in 2017. The programme employed an emergency transportation system and technical and operational support of the health system to address the three delays leading to maternal and neonatal mortality. DESIGN: Cross-sectional, qualitative research with key healthcare system stakeholders from the national, regional and district levels. SETTING: The study was conducted in Kahama and Kishapu districts in Shinyanga, Tanzania. The two districts were selected purposefully to represent the programme implementation districts' rural and urban or semiurban settings. PARTICIPANTS: District, regional and national stakeholders involved in implementing the m-mama pilot programme in Shinyanga were interviewed between February and March 2022. RESULTS: Lessons learnt from implementing the m-mama programme were grouped into four key themes: community engagement, emergency transportation system, government engagement, and challenges and constraints in technical implementation. Stakeholder engagement and collaboration at all levels, community involvement in implementation, adherence to local contexts and effective government partnerships were identified as key drivers for programme success. Coordination, supervision and infrastructure enhancement were crucial in implementing the emergency transportation system. CONCLUSIONS: Facilitating community involvement, understanding the local context and adapting to existing structures can enhance programme ownership and utilisation. The government serves as the central coordinator, overseeing resource mobilisation and distribution. A well-executed and coordinated emergency transportation system holds promise in addressing delays and curbing maternal and neonatal mortality. Collaborative knowledge-sharing among implementers is essential for identifying best practices and gaining insights into practical strategies for addressing anticipated challenges.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".