Étude des facteurs contributifs de l’accouchement à domicile (Sénégal)
Bibliographic record
Abstract
In Senegal, 38% of deliveries occur at home. We believe that preparation for childbirth, informing women about the place of birth and the person who will assist in delivery, and the necessity of saving money in case of complications, can reduce deliveries at home. The purpose of this study is to determine the impact of preparation for delivery on place of delivery in Senegal. Matching was done on the preparation for delivery by the propensity score using the R package Matchit. A conditional logistic regression was used to analyze the relationship between preparation for birth and place of birth. The data were collected in 2006 from a sample of 3,093 women aged 15 to 49 years, mothers of children from 0 to 23 months in 5 regions of Senegal. The average age of women was 26.3 years (±6.6). The prevalence of delivery at home was 0.33 and 0.31 were given a preparation for childbirth. The following factors were associated with childbirth at home: preparing for the birth (OR: 0.36, CI 95%: [0.28-0.45]), at least primary school (OR: 0.59, CI 95%: [0.46-0.74]), number of prenatal care >3 (OR: 0.40, CI 95%: [0.29-0.54]) and early prenatal care (OR: 0.69 [0.51-0.83]). The relation with the profession of the person who performed the prenatal consultation was of borderline significance (P = 0.06). Particular emphasis should be placed on the preparation of delivery, especially during prenatal consultations.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".