Implementing the ten steps to successful breastfeeding in multiple hospitals serving low-wealth patients in the US: innovative research design and baseline findings
Bibliographic record
Abstract
BACKGROUND: The Ten Steps to Successful Breastfeeding are maternity practices proven to support successful achievement of exclusive breastfeeding. They also are the basis for the WHO/UNICEF Baby-Friendly Hospital Initiative (BFHI). This study explores implementation of these steps in hospitals that serve predominantly low wealth populations. METHODS: A quasi-experimental design with mixed methods for data collection and analysis was included within an intervention project. We compared the impact of a modified Ten Steps implementation approach to a control group. The intervention was carried out in hospitals where: 1) BFHI designation was not necessarily under consideration, and 2) the majority of the patient population was low wealth, i.e., eligible for Medicaid. Hospitals in the research aspect of this project were systematically assigned to one of two groups: Initial Intervention or Initial Control/Later Intervention. This paper includes analyses from the baseline data collection, which consisted of an eSurvey (i.e., Carolina B-KAP), Maternity Practices in Infant Nutrition and Care survey tool (mPINC), the BFHI Self-Appraisal, key informant interviews, breastfeeding data, and formatted feedback discussion. RESULTS: Comparability was ensured by statistical and non-parametric tests of baseline characteristics of the two groups. Additional findings of interest included: 1) a universal lack of consistent breastfeeding records and statistics for regular monitoring/review, 2) widespread misinterpretation of associated terminology, 3) health care providers' reported practices not necessarily reflective of their knowledge and attitudes, and 4) specific steps were found to be associated with hospital breastfeeding rates. A comprehensive set of facilitators and obstacles to initiation of the Ten Steps emerged, and hospital-specific practice change challenges were identified. DISCUSSION: This is one of the first studies to examine introduction of the Ten Steps in multiple hospitals with a control group and in hospitals that were not necessarily interested in BFHI designation, where the population served is predominantly low wealth, and with the use of a mixed methods approach. Limitations including numbers of hospitals and inability to adhere to all elements of the design are discussed. CONCLUSIONS: For improvements in quality of care for breastfeeding dyads, innovative and site-specific intervention modification must be considered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".