Assessment of the <scp>Baby‐Friendly</scp> Hospital Initiative showed suboptimal knowledge and a low exclusive breastfeeding rate in Ogun State, Nigeria
Bibliographic record
Abstract
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.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".