In-Hospital Formula Supplementation of Healthy Breastfeeding Newborns
Bibliographic record
Abstract
The UNICEF/WHO Baby-Friendly Hospital Initiative suggests that breastfeeding activities in hospital are important to later breastfeeding. Understanding reasons for in-hospital supplementation may help to optimize the successful implementation of this initiative. The objective was to identify predictors of in-hospital initial formula supplementation of healthy, breastfeeding newborns. The authors analyzed 564 Canadian mother-infant pairs and interviewed nurses. Half of the study infants (47.9%) received formula in hospital; the median age at first supplementation was 8.4 hours. Risk for supplementation was affected by birth occurring between 7 PM and 9 AM (hazard ratio [HR] varied with time) and high maternal trait anxiety (HR=1.61, 95% confidence interval [CI]=1.01, 2.59). The following variables were protective against supplementation: planning to exclusively breastfeed (HR=0.46, 95% CI=0.33, 0.64), planning to breastfeed for >or=3 months (HR=0.56, 95% CI=0.37-0.86), childbirth education (HR=0.61, 95% CI=0.43, 0.86), mother born in Canada (HR=0.68, 95% CI=0.53, 0.87), completion of community college (HR=0.76, 95% CI=0.59, 0.98), male infant (HR=0.78, 95% CI=0.61, 0.99), and breastfeeding at delivery (HR varied with time). Nurses reported breastfeeding problems, infant behavior, and maternal fatigue as reasons for supplementing. Reassessing patterns of night feeds and encouraging breastfeeding at delivery may decrease supplementation. Trait anxiety reduction and the role of infant gender in supplementation merit further study.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".