Assessing the impact of a statewide effort to improve breastfeeding rates: A RE‐AIM evaluation of CHAMPS in Mississippi
Bibliographic record
Abstract
Communities and Hospitals Advancing Maternity Practices (CHAMPS) is a public health initiative, operating in Mississippi since 2014, to improve maternal and child health practices and reduce racial disparities in breastfeeding. Using the Reach, Effectiveness, Adoption, Implementation and Maintenance framework, this study assessed CHAMPS, which used a Quality Improvement intervention at hospitals, and engaged intensively with local community partners. The study team assessed outcomes through quantitative data (2014-2020) from national sources, Mississippi hospitals, community partners and CHAMPS programme records, and qualitative data from focus groups. With 95% of eligible Mississippi hospitals enrolled into CHAMPS, the programme reached 98% of eligible birthing women in Mississippi, and 65% of breastfeeding peer counsellors in Mississippi's Special Supplemental Nutrition Programme for Women, Infants and Children. Average hospital breastfeeding initiation rates rose from 56% to 66% (p < 0.05), the proportion of hospitals designated Baby-Friendly or attaining the final stages thereof rose from 15% to 90%, and 80% of Mississippi Special Supplemental Programme for Women, Infants, and Children districts engaged with CHAMPS. CHAMPS also maintains a funded presence in Mississippi, and all designated hospitals have maintained Baby-Friendly status. These findings show that a breastfeeding-focused public health initiative using broad-based strategic programming involving multiple stakeholders and a range of evaluation criteria can be successful. More breastfeeding promotion and support programmes should assess their wider impact using evidence-based implementation frameworks.
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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 | 0.024 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".