Hospital re-admission of late preterm or term infants is not a factor influencing duration of predominant breastfeeding
Bibliographic record
Abstract
OBJECTIVE: To determine whether hospital re-admission within the first 2 months of life decreases the odds of predominant breastfeeding. DESIGN: Mothers living in two large healthcare regions of Alberta (population 1 000 000 each) were recruited to participate in this prospective matched cohort study if they delivered a singleton infant between 34 and 41 weeks' gestation and were discharged within 7 days. Re-admitted infants were matched to non-re-admitted infants by site and date of birth. Questionnaires were mailed at 2 months postpartum. Predominant breastfeeding was defined as breastfeeding for at least three feedings per day for the past 7 days. RESULTS: A total of 1798 mothers were eligible for analysis, (n=250 re-admitted, 1548 non-re-admitted). Seventy three per cent (n=1315) reported predominant breastfeeding at 2 months. Infant re-admission (adjusted OR: 1.12, 95% CI 0.8 to 1.55) and late preterm birth were not associated with discontinuation of predominant breastfeeding. The odds of predominantly breastfeeding were two times greater, if mothers' perceptions of talking about breastfeeding with a healthcare provider were positive versus negative. Whereas the odds were decreased for primiparous women (adjusted OR 0.61 95% CI 0.47 to 0.78) and not impacted for multiparous women (OR 0.60 95% CI 0.32 to 1.13) with a negative versus neutral perception of the breastfeeding talk experience. CONCLUSIONS: Hospital re-admission and late preterm birth had no significant impact on the odds of predominant breastfeeding beyond 8 weeks post partum whereas the odds were increased with a perception of a positive experience in speaking with a healthcare provider.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".