Implementation of a Community Transport Strategy to Reduce Delays in Seeking Obstetric Care in Rural Mozambique
Bibliographic record
Abstract
INTRODUCTION: Delays due to long distances to health facilities, poor road infrastructure, and lack of affordable transport options contribute to the burden of maternal deaths in Mozambique. This study aimed to assess the implementation and uptake of an innovative community-based transport program to improve access to emergency obstetric care in southern Mozambique. METHODS: From April 2016 to February 2017, a community transport strategy was implemented as part of the Community Level Interventions for Pre-eclampsia Trial. The study aimed to reduce maternal and perinatal mortality and morbidity by 20% in intervention clusters in Maputo and Gaza Provinces, Mozambique, by involving community health workers in the identification and referral of pregnant and puerperal women at risk. Based on a community-based participatory needs assessment, the transport program was implemented with the trial. Demographics, conditions requiring transportation, means of transport used, route, and outcomes were collected during implementation. Data were entered into a REDCap database. RESULTS: Fifty-seven neighborhoods contributed to the needs assessment; of those, 13 (23%) implemented the transport program. Neighborhoods were selected based on their expression of interest and ability to contribute financially to the program (US$0.33 per family per month). In each selected neighborhood, a community management committee was created, training in small-scale financial management was conducted, and monitoring tools were provided. Twenty people from 9 neighborhoods benefited from the transport program, 70% were pregnant and postpartum women. CONCLUSION: These results demonstrate that it was feasible to implement a community-based transport program with no external input of vehicles, fuel, personnel, and maintenance. However, high cost and a lack of acceptable transport options in some communities continue to impede access to obstetric health care services and the ability for timely follow-up. When strengthening capacities of community health workers to promptly assist and refer emergency cases, it is crucial to encourage local transport programs and transportation infrastructure among minimally resourced communities to support access and engagement with health systems.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".