Determinants of Childbirth Choice in Rural Senegal: Mixed-Methods Analysis Using Data from the Niakhar Demographic Surveillance System
Bibliographic record
Abstract
Background: Faced with high rates of home births and maternal mortality, the Senegalese government has made enormous efforts to improve the provision of care. However, the use of health facilities for home births remains a challenge. Materials and methods: The study used a mixed-methods approach. Data from women who gave birth in the Niakhar observatory area between 1983 and 2020 were used, and chi-square tests and qualitative analyses were performed. Results: The results show that all variables are significant at the 0.5% level. Women giving birth at home were those who were married, griottes, poor, uneducated, aged 35-49, had given birth more than four times and had had fewer than two antenatal consultations. Qualitative results showed that physical condition, lack of understanding, privacy concerns of older women and economic barriers all contributed to the increase in the phenomenon. Conclusion: In rural areas, health problems are complex and require a more integrated approach. Determinants can run counter to the public policies put in place. It is therefore essential, when developing health policies, to integrate measures adapted to the type of population.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".