Ohio First Steps for Healthy Babies: A Program Supporting Breastfeeding Practices in Ohio Birthing Hospitals
Bibliographic record
Abstract
Background: Ohio First Steps for Healthy Babies (First Steps) is a free, voluntary statewide designation program coadministered by the Ohio Department of Health and the Ohio Hospital Association that promotes breastfeeding-supportive maternity practices aligned with the Baby-Friendly Hospital Initiative (BFHI).Materials and Methods: We examined Ohio birthing hospitals’ participation in First Steps, and changes in breastfeed-ing rates at hospital discharge, over the first 12 quarters of the program (July 15, 2015, to July 14, 2018) for all 110 licensed Ohio birthing hospitals. The 81 (73.6%) that achieved at least 1 step over the study period (designated as First Steps hospitals) were compared to the 29 non-First Steps hospitals, and the 17 that began participation at First Steps startup (July 15, 2015) were identified for additional analysis. Changes in breastfeeding rates were examined using a mixed effects multivariate regression model.Results: Breastfeeding increased significantly over the program period from 73.8% to 76.7% (mean 0.19% per quarter, p = .0002), but without a significant difference in breastfeeding rates between First Steps and non-First Steps hospitals. However, in a pre- and post-program analysis for the 17 hospitals that began participation at First Steps startup (excluding an additional 6 hospitals with BFHI designation), number of quarters in the program, number of steps completed, and number of births in 2015 were significantly associated with breastfeeding rates. Hospitals that completed at least 2 steps every 5 quarters in the First Steps program increased breastfeeding when compared to those not participating in the program.Conclusion: These encouraging results provide a formal evaluation of a best practices BFHI-modelled statewide program.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".