www.mdpi.com/journal/ijerph Article Lessons Learned from the Implementation of a Provincial Breastfeeding Policy in Nova Scotia, Canada and the Implications for Childhood Obesity Prevention
Bibliographic record
Abstract
Abstract: Healthy public policy plays a central role in creating environments that are supportive of health. Breastfeeding, widely supported as the optimal mode for infant feeding, is a critical factor in promoting infant health. In 2005, the Canadian province of Nova Scotia introduced a provincial breastfeeding policy. This paper describes the process and outcomes of an evaluation into the implementation of the policy. This evaluation comprised focus groups held with members of provincial and district level breastfeeding committees who were tasked with promoting, protecting and supporting breastfeeding in their districts. Five key themes were identified, which were an unsupportive culture of breastfeeding; the need for strong leadership; the challenges in engaging physicians in dialogue around breastfeeding; lack of understanding around the International Code of Marketing of Breast-milk Substitutes; and breastfeeding as a way to address childhood obesity. Recommendations for other jurisdictions include the need for a policy, the value of leadership, the need to integrate policy with other initiatives across sectors and the importance of coordination and support at multiple levels. Finally, promotion of breastfeeding offers a population-based strategy for addressing the childhood obesity epidemic and
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".