Hospital practices to promote breastfeeding: The effect of maternal age
Bibliographic record
Abstract
BACKGROUND: Breastfeeding rates are disproportionately low among young mothers in the United States. Although the use of hospital practices to promote breastfeeding is widely supported, the extent to which these practices help explain breastfeeding disparities by maternal age is unclear. Accordingly, we aimed to explore how maternal age may affect (1) receipt of hospital practices and (2) associations between these practices and exclusive breastfeeding. METHODS: Data were derived from participants (n = 1598) of Listening to Mothers III, a national survey administered to mothers of singleton births in United States hospitals from July 2011 to June 2012. We used multivariable logistic regression models and interaction terms to examine maternal age as an effect modifier. RESULTS: Compared with mothers aged 30 and older, mothers aged 18-19 had lower odds of reporting that nurses helped them initiate breastfeeding when ready (OR 0.59 [95% CI 0.35-0.99]), they roomed-in with their baby (OR 0.32 [95% CI 0.19-54]) and they did not receive a pacifier (OR 0.53 [95% CI 0.32-0.90]). Many associations with breastfeeding were stronger among mothers aged 18-19 and 20-24 than mothers aged 25-29 and 30 and older. Additionally, compared with receiving a pacifier, not receiving a pacifier was associated with greater odds of exclusive breastfeeding at 1 week among mothers aged 30 and older (OR 1.47 [95% CI 1.02-2.11]) but lower odds among mothers aged 18-19 (OR 0.26 [95% CI 0.10-0.70]). CONCLUSIONS: Hospital practices to promote breastfeeding may be differentially implemented by maternal age. Encouraging teenage mothers to room-in with their babies may be particularly important for reducing breastfeeding disparities. Pacifier use among babies of teenage mothers requires further exploration.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".