The process of implementing a new baby-friendly hospital initiative for nurses: a grounded theory study
Bibliographic record
Abstract
Maternity nursing practice is changing across Canada with the movement toward becoming “baby friendly.” The World Health Organization (WHO) recommends the Baby-Friendly Hospital Initiative (BFHI) as a standard of care in hospitals worldwide. Very little research has been conducted with nurses to explore the impact of the initiative on nursing practice. The purpose of this study, therefore, was to examine the process of implementing the BFHI for nurses. The study was carried out using Corbin and Strauss’s method of grounded theory. Theoretical sampling was employed, which resulted in recruiting and interviewing 13 registered nurses whose area of employment included neonatal intensive care, postpartum, and labour and delivery. The data analysis revealed a central category of resisting the BFHI. All of the nurses disagreed with some of the 10 steps to becoming a baby-friendly hospital as outlined by the WHO. Participants questioned the science and safety of aspects of the BFHI. Also, participants indicated that the implementation of this program did not substantially change their nursing practice. They empathized with new mothers and anticipated being collectively reprimanded by management should they not follow the initiative. Five conditions influenced their responses to the initiative, which were (a) an awareness of a pro-breastfeeding culture, (b) imposition of the BFHI, (c) knowledge of the health benefits of breastfeeding, (d) experiential knowledge of infant feeding, and (e) the belief in the autonomy of mothers to decide about infant feeding. The identified outcomes were moral distress and division between nurses. The study findings could guide decision making concerning the implementation of the 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 | 0.044 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| 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".