Changes in caesarean section rates and milk feeding patterns of infants between 1986 and 2013 in the Dominican Republic
Bibliographic record
Abstract
OBJECTIVE: The relationship between caesarean sections (C-sections) and infant feeding varies between different samples and indicators of feeding. The current study aimed to determine the relationship between C-sections and five indicators of infant milk feeding (breast-feeding within 1 h after delivery, at the time of the survey (current) and ever; milk-based prelacteal feeds; and current non-breast milk use) over time in a country with a rapidly rising C-section rate. DESIGN: Secondary data analysis on cross-sectional data from Demographic and Health Surveys from six different time points between 1986 and 2013. SETTING: Dominican Republic. SUBJECTS: Infants under 6 months of age. RESULTS: Over 90 % of infants were ever breast-fed in each survey sample. However, non-breast milk use has expanded over time with a concomitant drop in predominant breast-feeding. C-section prevalence has increased over time reaching 63 % of sampled infants in the most recent survey. C-sections remained significantly related to three infant feeding practices - the child not put to the breast within 1 h after delivery, milk-based prelacteal feeds and current non-breast milk use - in multivariate models that included sociodemographic control variables. However, current non-breast milk use was no longer related to C-sections when milk-based prelacteal feeds were factored into the model. CONCLUSIONS: Reducing or avoiding milk-based prelacteal feeds, particularly among those having C-sections, may improve subsequent breast-feeding patterns. Simultaneously, efforts are needed to understand and help reduce the exceptionally high C-section rate in the Dominican Republic.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".