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Record W4388290705 · doi:10.26685/urncst.516

The Baby Friendly Hospital Initiative in Canada: A Narrative Review

2023· review· en· W4388290705 on OpenAlexaffabout
Faye Arellano

Bibliographic record

VenueUndergraduate Research in Natural and Clinical Science and Technology (URNCST) Journal · 2023
Typereview
Languageen
FieldMedicine
TopicBreastfeeding Practices and Influences
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBreastfeedingAccreditationMedicinePandemicFamily medicineCoronavirus disease 2019 (COVID-19)PediatricsNursingEnvironmental healthMedical education

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.006
Science and technology studies0.0010.005
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.009
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.134
GPT teacher head0.487
Teacher spread0.354 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations3
Published2023
Admission routes2
Has abstractyes

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