The role of transportation to access maternal care services for women in rural Bangladesh and Burkina Faso: A mixed methods study
Bibliographic record
Abstract
OBJECTIVE: To understand the role of transportation in accessing health care during pregnancy, delivery, and the postpartum period among women in rural Bangladesh and Burkina Faso. METHODS: An exploratory mixed methods study was conducted in Mymensingh district in Bangladesh and Kaya district in Burkina Faso. We recruited 300 women from Bangladesh and 340 from Burkina Faso with a delivery outcome within one year of interview. Key informant interviews were conducted with 19 participants and 12 focus group discussions took place with attendees in selected community clinics. RESULTS: Of the interviewees, 45.7% in Bangladesh and 73.2% in Burkina Faso reported having had health complications during their last pregnancy, delivery, or postpartum period. Of all women, 42.7% in Bangladesh and 67.4% in Burkina Faso sought facility care for their complications. Facility-based delivery was much higher in Burkina Faso (87.7%) than Bangladesh (38.2%). Literacy, transport availability, transportation costs, and travel time were associated with care seeking behavior. CONCLUSION: Lack of reliable transportation was reported as a significant barrier to accessing care during pregnancy, delivery, and postpartum by women in Bangladesh and Burkina Faso. Effort should be made to improve access to emergency obstetric care, and transport intervention should be strengthened.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".