Promoting booster seat use for young children: A school-based intervention pilot study
Bibliographic record
Abstract
PURPOSE: Misuse and/or lack of booster seat use are often associated with high rates of injury and death among school-aged children. This pilot study examined the efficacy and the potential effectiveness of a booster seat intervention in the classroom. METHODS: Two elementary schools participated (randomly assigned as one intervention school and one control school). At the intervention school, a certified car seat specialist and a police officer held an interactive booster seat session. The height and age for each child were recorded. Children received a certificate indicating whether they met the requirements for booster seat use and a postcard with car seat restraint specifications. Children in the control school received a brochure on car seat safety. Pre- and post-intervention self-reports were collected and booster seat use was observed. RESULTS: Observational findings showed a decline in booster seat use at the control school and an increase in use at the intervention school. Self-reports of booster seat use indicated a decline at both schools; however, cell sizes were too small to permit statistical analyses. CONCLUSION: Anecdotally researchers found the sessions were easy to conduct and were well received by the children and could be easily integrated into programming in schools. Classroom sessions may have the potential to positively influence booster seat use among 6- to 8-year-olds.
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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 | 0.003 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".