Promoting Safe Sleep, Tobacco Cessation, and Breastfeeding to Rural Women During the COVID-19 Pandemic: Quasi-Experimental Study
Bibliographic record
Abstract
BACKGROUND: Safe Sleep Community Baby Showers address strategies to prevent sleep-related infant deaths. Due to the COVID-19 pandemic, these events transitioned from in-person to virtual. OBJECTIVE: This study describes outcomes of transitioning Safe Sleep Community Baby Showers to a virtual format and compares outcomes to previous in-person events. METHODS: Participants from four rural Kansas counties were emailed the presurvey, provided educational materials (videos, livestream, or digital documents), and completed a postsurvey. Those who completed both surveys received a portable crib and wearable blanket. Within-group comparisons were assessed between pre- and postsurveys; between-group comparisons (virtual vs in-person) were assessed by postsurveys. RESULTS: Based on data from 145 in-person and 74 virtual participants, virtual participants were more likely to be married (P<.001) and have private insurance (P<.001), and were less likely to report tobacco use (P<.001). Both event formats significantly increased knowledge and intentions regarding safe sleep and avoidance of secondhand smoke (all P≤.001). Breastfeeding intentions did not change. Differences were observed between in-person and virtual meetings regarding confidence in the ability to avoid secondhand smoke (in-person: 121/144, 84% vs virtual: 53/74, 72%; P=.03), intention to breastfeed ≥6 months (in-person: 79/128, 62% vs virtual: 52/66, 79%; P=.008), and confidence in the ability to breastfeed ≥6 months (in-person: 58/123, 47% vs virtual: 44/69, 64%; P=.02). CONCLUSIONS: Although both event formats demonstrated increased knowledge/intentions to follow safe sleep recommendations, virtual events may further marginalize groups who are at high risk for poor birth outcomes. Strategies to increase technology access, recruit priority populations, and ensure disparities are not exacerbated will be critical for the implementation of future virtual events.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".