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Record W4389140199 · doi:10.1111/apa.17051

Assessment of the <scp>Baby‐Friendly</scp> Hospital Initiative showed suboptimal knowledge and a low exclusive breastfeeding rate in Ogun State, Nigeria

2023· article· en· W4389140199 on OpenAlexaboutno aff
Rafiq A. Abdul, Catherine O. Agbede, AO Adekoya, Olufunmilola Abolurin, Olufunke O. Obadina

Bibliographic record

VenueActa Paediatrica · 2023
Typearticle
Languageen
FieldMedicine
TopicBreastfeeding Practices and Influences
Canadian institutionsnot available
Fundersnot available
KeywordsBreastfeedingOgun stateHealth careNursingQuarter (Canadian coin)MedicineHealth facilityFamily medicinePediatricsEnvironmental healthEconomic growthPopulationPolitical scienceGeographyHealth servicesLocal government

Abstract

fetched live from OpenAlex

AIM: Implementing the Baby-Friendly Hospital Initiative (BFHI) programme has been fraught with challenges globally. The study aimed to assess the implementation of the BFHI and breastfeeding practices in healthcare facilities in Ogun State, Nigeria. METHODS: It was a questionnaire-based cross-sectional study carried out between August and October 2019 among 100 healthcare workers and 110 mothers from health facilities in Ijebu-Ode Local Government Area of Ogun State, Nigeria. RESULTS: Nearly two-thirds (61.0%) of the healthcare workers were community health workers while the others were nurses. Less than a quarter (23.8%) of the healthcare workers had ever attended breastfeeding educational programmes since they started working. About half of the healthcare workers had good knowledge, attitude and practice of BFHI. Nurses had a significantly better practice of BFHI than community health workers. Understaffing was a major limitation to the implementation of BFHI. The mothers had an exclusive breastfeeding rate of 47%. CONCLUSION: The knowledge, attitude, and practice of BFHI were suboptimal and the exclusive breastfeeding rate among the mothers was low. There is a need to improve staff strength, training and re-training of staff, as well as regular monitoring and evaluation of healthcare facilities on the implementation of BFHI.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.807

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.289
Teacher spread0.276 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations2
Published2023
Admission routes1
Has abstractyes

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