Towards sustainable emergence transportation system for maternal and new born: Lessons from the m-mama innovative pilot program in Shinyanga, Tanzania
Bibliographic record
Abstract
Maternal mortality comprises about 10% of all deaths among women of reproductive age (15-49 years). More than 90% of such deaths occur in low- and middle-income countries (LMIC). In this study, we aimed to document lessons learnt and best practices toward sustainability of the m-mama program for reducing maternal and newborn mortality in Tanzania. We conducted a qualitative study from February to March 2022 in Kahama and Kishapu district councils of Shinyanga region. A total of 20 Key Informant Interviews (KII) and four Focused Group Discussions (FGDs) were conducted among key stakeholders. The participants included implementing partners and beneficiaries, Community Care groups (CCGs) facilitators, health facility staff, drivers and dispatchers. We gathered data on their experience with the program, services offered, and recommendations to improve program sustainability. We based the discussion of our findings on the integrated sustainability framework (ISF). Thematic analysis was conducted to summarize the results. To ensure the sustainability of the program, these were recommended. First, active involvement of the government to complement community efforts, through the provision and maintenance of resources including a timely and inclusive budget, dedicated staff, infrastructure development and maintenance. Secondly, support from different stakeholders through a well-coordinated partnership with the government and local facilities. Third, continued capacity building for implementers, health care workers (HCWs) and community health workers (CHWs) and community awareness to increase program trust and services utilization. Dissemination and sharing of evidence and lesson learnt from successful program activities and close monitoring of implemented activities is necessary to ensure smooth, well-coordinated delivery of proposed strategies. Considering the temporality of the external funding, for successful implementation of the program, we propose a package of three key actions; first, strengthening government ownership and engagement at an earlier stage, secondly, promoting community awareness and commitment and lastly, maintaining a well-coordinated multi-stakeholder' involvement during program implementation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 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".