Determinants of non‐medically indicated cesarean deliveries in Burkina Faso
Bibliographic record
Abstract
OBJECTIVE: To identify the factors associated with non-medically indicated cesarean deliveries (NMIC) in Burkina Faso in centers where user fees for cesarean delivery were partially removed. METHODS: We carried out a criteria-based audit in 22 referral hospitals, using data from a 6-month prospective observational study, to assess the proportion of NMIC. Multivariate logistic regression analyses were used to identify factors associated with NMIC. RESULTS: The decision of cesarean delivery was not medically indicated in 24% of cases. The factors independently associated with NMIC were urban residence (adjusted OR 1.55; 95% CI, 1.12-2.12; P=0.006), spouse's occupation other than breeder or farmer (aOR varying from 1.77 [95% CI, 1.19-2.62] to 2.15 [95% CI, 1.38-3.32] according to the profession), and cesarean decided by a general practitioner (aOR 1.61; 95% CI, 1.13-2.30; P=0.009). CONCLUSION: The high percentage of unnecessary cesarean deliveries is in contrast to the unmet needs of women who still deliver outside health facilities. NMIC is associated with both socioeconomic determinants and medical factors. Hence, interventions are needed to improve the skills of healthcare professionals and awareness of women concerning the risks associated with unnecessary cesarean delivery.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".