Understanding home delivery in a context of user fee reduction: a cross-sectional mixed methods study in rural Burkina Faso
Bibliographic record
Abstract
BACKGROUND: Several African countries have recently reduced/removed user fees for maternal care, producing considerable increases in the utilization of delivery services. Still, across settings, a conspicuous number of women continue to deliver at home. This study explores reasons for home delivery in rural Burkina Faso, where a successful user fee reduction policy is in place since 2007. METHODS: The study took place in the Nouna Health District and adopted a triangulation mixed methods design, combining quantitative and qualitative data collection and analysis methods. The quantitative component relied on use of data from the 2011 round of a panel household survey conducted on 1130 households. We collected data on utilization of delivery services from all women who had experienced a delivery in the previous twelve months and investigated factors associated with home delivery using multivariate logistic regression. The qualitative component relied on a series of open-ended interviews with 55 purposely selected households and 13 village leaders. We analyzed data using a mixture of inductive and deductive coding. RESULTS: Of the 420 women who reported a delivery, 47 (11 %) had delivered at home. Random effect multivariate logistic regression revealed a clear, albeit not significant trend for women from a lower socio-economic status and living outside an area to deliver at home. Distance to the health facility was found to be positively significantly associated with home delivery. Qualitative findings indicated that women and their households valued facility-based delivery above home delivery, suggesting that cultural factors do not shape the decision where to deliver. Qualitative findings confirmed that geographical access, defined in relation to the condition of the roads and the high transaction costs associated with travel, and the cost-sharing fees still applied at point of use represent two major barriers to access facility-based delivery. CONCLUSIONS: Findings suggest that the current policy in Burkina Faso, as similar policies in the region, should be expanded to remove fees at point of use completely and to incorporate benefits/solutions to support the transport of women in labor to the health facility in due time.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".