Breastfeeding in Canada: predictors of initiation, exclusivity, and continuation from the 2017–2018 Canadian Community Health Survey
Bibliographic record
Abstract
Human milk is the ideal source of nutrition for infants; however, adherence to breastfeeding recommendations is suboptimal and availability of Canadian breastfeeding data are limited. Using the 2017–2018 Canadian Community Health Survey Public Use Microdata File (Maternal Experiences Module, n = 5558, weighted n = 1 669 462) we computed breastfeeding indicators and explored sociodemographic, health, and geographical predictors of breastfeeding with univariate logistic regression models. Nationally, of all participants who gave birth in the preceding 5 years, 91% initiated breastfeeding, 43% exclusively breastfed to ≥5 months and 35% to ≥6 months, 56% reported any breastfeeding at ≥6 months, and 31% reported breastfeeding at ≥12 months. Breastfeeding cessation was most commonly attributed to insufficient milk supply (25%), but reasons differed significantly by breastfeeding duration. Breastfeeding initiation, exclusivity for ≥5 months, and extended breastfeeding ≥12 months all differed by geographic region, and by most sociodemographic and health characteristics. Positive breastfeeding outcomes were highest in British Columbia, and lowest in Quebec and the Atlantic region, and generally higher if caregivers had recently immigrated to Canada, were married, were >30 years of age, were not White, were nonsmoking, had completed postsecondary education, and had an annual household income >$40 000. These disparities indicate the need for tailored, equitable approaches to breastfeeding support, and continued regional monitoring of breastfeeding outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".