Bibliographic record
Abstract
Breastfeeding offers substantial benefits to infant health, encompassing physical and neurodevelopmental aspects. National and international guidelines, such as those from the Canadian Paediatric Society (CPS) and World Health Organization (WHO), recommend exclusive breastfeeding for the first six months of life, followed by continued breastfeeding with complementary foods until two years of age or beyond. Despite these recommendations, Canada faces challenges in achieving optimal breastfeeding rates, with only 35% of parents exclusively breastfeeding until the recommended six-month mark. This narrative review aims to assess the implementation rate of the Baby-Friendly Hospital Initiative (BFHI) in Canada, an intervention established by the WHO and the United Nations Children’s Fund (UNICEF) to promote breastfeeding. Comprehensive searches on Google and official websites of relevant associations and organizations were conducted to gather data on the number of designated Baby-Friendly Hospitals using reports from 2016 to 2022. Our findings reveal that only 3% of the 604 total hospitals in Canada available for receiving Baby-Friendly designation have acquired it. Furthermore, the proportion of designated hospitals is less than one-third in each province. There are varying trends in the number of designated Baby-Friendly Hospitals across Canadian provinces and territories. While some have demonstrated a steady increase over the examined period (e.g. Nova Scotia, Alberta), others exhibited a decline or no change (e.g. Ontario, Prince Edward Island). Several factors may have contributed to the low numbers and trends in BFHI designation, including the COVID-19 pandemic, lack of BFHI implementation in hospital accreditation requirements, and the dispersed efforts towards BFHI-related activities. These results underscore the urgent need for enhanced implementation of the BFHI across Canada.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".