Effects of Nurse Staffing on Missed Breastfeeding Support in Maternity Units With Different Nurse Work Environments
Bibliographic record
Abstract
PURPOSE: To examine the effect of nurse staffing in varying work environments on missed breastfeeding teaching and support in inpatient maternity units in the United States. BACKGROUND: Breast milk is the optimal food for newborns. Teaching and supporting women in breastfeeding are primarily a nurse's responsibility. Better maternity nurse staffing (fewer patients per nurse) is associated with less missed breastfeeding teaching and support and increased rates of breastfeeding. We examined the extent to which the nursing work environment, staffing, and nurse education were associated with missed breastfeeding care and how the work environment and staffing interacted to impact missed breastfeeding care. METHODS: In this cross-sectional study using the 2015 National Database of Nursing Quality Indicator survey, maternity nurses in hospitals in 48 states and the District of Columbia responded about their workplace and breastfeeding care. Clustered logistic regression models with interactions were used to estimate the effects of the nursing work environment and staffing on missed breastfeeding care. RESULTS: There were 19 486 registered nurses in 444 hospitals. Nearly 3 in 10 (28.2%) nurses reported missing breastfeeding care. In adjusted models, an additional patient per nurse was associated with a 39% increased odds of missed breastfeeding care. Furthermore, 1 standard deviation decrease in the work environment was associated with a 65% increased odds of missed breastfeeding care. In an interaction model, staffing only had a significant impact on missed breastfeeding care in poor work environments. CONCLUSIONS: We found that the work environment is more fundamental than staffing for ensuring that not only breastfeeding care is not missed but also breastfeeding care is sensitive to nurse staffing. Improvements to the work environment support the provision of breastfeeding care. IMPLICATIONS FOR RESEARCH AND PRACTICE: Both nurse staffing and the work environment are important for improving breastfeeding rates, but the work environment is foundational.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".