Factors Influencing the Intention of Perinatal Nurses to Adopt the Baby-Friendly Hospital Initiative in Southeastern Quebec, Canada: Implications for Practice
Bibliographic record
Abstract
Nurses play a major role in promoting the baby-friendly hospital initiative (BFHI), yet the adoption of this initiative by nurses remains a challenge in many countries, despite evidences of its positive impacts on breastfeeding outcomes. The aim of this study was to identify the factors influencing perinatal nurses to adopt the BFHI in their practice. Methods. A sample of 159 perinatal nurses from six hospital-based maternity centers completed a survey based on the theory of planned behavior. Hierarchical multiple linear regression analyses were performed to assess the relationship between key independent variables and nurses' intention to adopt the BFHI in their practice. A discriminant analysis of nurses' beliefs helped identify the targets of actions to foster the adoption the BFHI among nurses. Results. The participants are mainly influenced by factors pertaining to their perceived capacity to overcome the strict criteria of the BFHI, the mothers' approval of a nursing practice based on the BFHI, and the antenatal preparation of the mothers. Conclusions. This study provides theory-based evidence for the development of effective interventions aimed at promoting the adoption of the BFHI in nurses' practice.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".