Predictors of breast-feeding in a developing country: results of a prospective cohort study
Bibliographic record
Abstract
OBJECTIVE: Data on the prevalence and predictors of breast-feeding remain scarce in Lebanon. Moreover, no study has previously addressed the effect of the paediatrician's sex on breast-feeding. The present study aimed to assess the prevalence and predictors of breast-feeding at 1 and 4 months of infant age while exploring the potential role of the sex of the paediatrician. DESIGN: Prospective cohort study. Predictors of breast-feeding significant at the bivariate level were tested at 1 and 4 months through two stepwise regression models. SETTING: Infants were enrolled through the clinics and dispensaries of 117 paediatricians located in Beirut, Lebanon, and its suburbs. SUBJECTS: A total of 1,320 healthy newborn infants born between August 2001 and February 2002 were prospectively followed during the first year. FINDINGS: Breast-feeding rates at 1 and 4 months were 56.3 % and 24.7 %, respectively. Early discharge, high parity and religion were significantly associated with higher breast-feeding rates at 1 and 4 months of age. Maternal age proved significant only at 1 month, while maternal working status and sex of the paediatrician were significant at 4 months. A novel finding of our study was the positive effect of female paediatricians on breast-feeding continuation until 4 months of age (OR = 1.49; 95 % CI 1.03, 2.15). CONCLUSIONS: Breast-feeding rates are low at 1 and 4 months of infant age in Beirut. Further research to investigate the interactions between female physicians and lactating mothers in maintaining breast-feeding in other populations is warranted. The results constitute the basis for designing interventions targeting policy makers, health professionals and mothers.
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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 | 0.002 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".