Women's intentions to breastfeed: a population‐based cohort study
Bibliographic record
Abstract
OBJECTIVE: Given that intention to breastfeed is a strong predictor of breastfeeding initiation and duration, the objectives of this study were to estimate the population-based prevalence and the factors associated with the intention to breastfeed. DESIGN: Retrospective population-based cohort study. SETTING: All hospitals in Ontario, Canada (1 April 2009-31 March 2010). POPULATION: Women who gave birth to live, term, singletons/twins. METHODS: Patient, healthcare provider, and hospital factors that may be associated with intention to breastfeed were analysed using univariable and multivariable regression. MAIN OUTCOME MEASURES: Population-based prevalence of intention to breastfeed and its associated factors. RESULTS: The study included 92,364 women, of whom 78,806 (85.3%) intended to breastfeed. The odds of intending to breastfeed were higher amongst older women with no health problems and women who were cared for exclusively by midwives (adjusted OR 3.64, 95% CI 3.13-4.23). Being pregnant with twins (adjusted OR 0.73, 95% CI 0.57-0.94), not attending antenatal classes (adjusted OR 0.58, 95% CI 0.54-0.62), having previous term or preterm births (adjusted OR 0.79, 95% CI 0.78-0.81, and adjusted OR 0.87, 95% CI 0.82-0.93, respectively), and delivering in a level-1 hospital (adjusted OR 0.85, 95% CI 0.77-0.93) were associated with a lower intention to breastfeed. CONCLUSIONS: In this population-based study ~85% of women intended to breastfeed their babies. Key factors that are associated with the intention to breastfeed were identified, which can now be targeted for intervention programmes aimed at increasing the prevalence of breastfeeding and improving overall child and maternal health.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".