Promoting Use of Booster Seats in Rural Areas Through Community Sports Programs
Bibliographic record
Abstract
BACKGROUND: Booster seats reduce mortality and morbidity for young children in car crashes, but use is low, particularly in rural areas. This study targeted rural communities in 4 states using a community sports-based approach. OBJECTIVE: The Strike Out Child Passenger Injury (Strike Out) intervention incorporated education about booster seat use in children ages 4-7 years within instructional baseball programs. We tested the effectiveness of Strike Out in increasing correct restraint use among participating children. METHODS: Twenty communities with similar demographics from 4 states participated in a nonrandomized, controlled trial. Surveys of restraint use were conducted before and after baseball season. Intervention communities received tailored education and parents had direct consultation on booster seat use. Control communities received only brochures. RESULTS: One thousand fourteen preintervention observation surveys for children ages 4-7 years (Intervention Group [I]: N = 511, Control [C]: N = 503) and 761 postintervention surveys (I: N = 409, C: N = 352) were obtained. For 3 of 4 states, the intervention resulted in increases in recommended child restraint use (Alabama +15.5%, Arkansas +16.1%, Illinois +11.0%). Communities in 1 state (Indiana) did not have a positive response (-9.2%). Overall, unadjusted restraint use increased 10.2% in intervention and 1.7% in control communities (P = .02). After adjustment for each state in the study, booster seat use was increased in intervention communities (Cochran-Mantel-Haenszel odds ratio 1.56, 95% confidence interval [1.16-2.10]). CONCLUSIONS: A tailored intervention using baseball programs increased appropriate restraint use among targeted rural children overall and in 3 of 4 states studied. Such interventions hold promise for expansion into other sports and populations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".