Public Attitudes Toward Breastfeeding in Public Places in Ottawa, Canada
Bibliographic record
Abstract
BACKGROUND: In Ontario, Canada, breastfeeding in public is a protected right, yet even with these laws, attitudes toward breastfeeding in public can serve as a barrier to breastfeeding. Research aim: This study assesses public support for breastfeeding in public among adults in Ottawa, Ontario, and examines sociodemographic associations with negative attitudes toward public breastfeeding. METHODS: Data from the 2015 Rapid Risk Factor Surveillance System (RRFSS), a population health telephone survey, were obtained for Ottawa. Adults ages 18 years and older were asked whether it was acceptable for a mother to breastfeed her baby in a restaurant and shopping mall ( n = 1,276). Descriptive statistics and regression were used to describe sociodemographic characteristics associated with negative attitudes. RESULTS: Overall, 75% of respondents agreed that it was acceptable for a mother to breastfeed her baby in both a restaurant and shopping mall (restaurant: 78%; shopping mall: 81%). Respondents who did not have children at home, were less educated, had a mother tongue language other than French or English and who were retirees were less likely to support breastfeeding in restaurants and shopping malls. In addition, women and immigrants living in Canada for more than 15 years were less likely to support breastfeeding in shopping malls. CONCLUSION: Despite a law to support public breastfeeding in Ontario, there is room to improve attitudes toward public breastfeeding. Increased public support for public breastfeeding can support women and children to achieve their feeding goals, particularly for those wanting to exclusively breastfeed.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".