Breastfeeding intention and early post-partum practices among overweight and obese women in Ontario: a selective population-based cohort study
Bibliographic record
Abstract
OBJECTIVE: To explore the relationship between overweight and obesity and breastfeeding behaviors, a cohort study was conducted among 22,131 women who delivered in Ontario hospitals between April 1 2008 and March 31 2010. METHODS: Data regarding maternal characteristics, maternal body mass index (BMI), infant characteristics, and breastfeeding practices were obtained through the Better Outcomes Registry & Network birth records Database. Multivariate linear regression analysis was used to determine the rates of three outcome measures - intention to breastfeed, exclusive breastfeeding in hospital, and exclusive breastfeeding upon discharge from hospital - between non-obese, overweight and obese patients. RESULTS: While overweight mothers have similar intentions to breastfeed compared to non-overweight mothers (OR 1.03 (0.87-1.21), obese mothers were less likely to intend to breastfeed (OR 0.84 (0.70-0.99). Overweight and obese mothers were less likely to exclusively breastfeed in hospital compared to non-overweight mothers (aOR 0.67 (0.60-0.75) and 0.67 (0.60-0.75), respectively), and overweight and obese mothers were less likely to exclusively breastfeed on discharge (aOR 0.68 (0.61-0.76) and 0.68 (0.61-0.76), respectively). CONCLUSIONS: This study highlights that while overweight and obese women may benefit more from exclusive breastfeeding compared to non-overweight women, they are less likely to exclusively breastfeed in the immediate post-partum period.
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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 | 0.001 | 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) | 0.001 | 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".