Child restraint systems: Understanding confidence in proper use and addressing the need for education
Bibliographic record
Abstract
Objective: To quantify the extent of proper local child restraint system (CRS) use and to better understand changes to the level of self-reported confidence with increased CRS installations. With the goal being to improve safety for children travelling in personal vehicles across London, ON and the region. Methods: Public CRS clinics were initiated by Injury Prevention staff after they obtained the Child Passenger Safety Technician certification. Additionally, an online survey was commissioned targeting Ontario parents who had installed at least one CRS in the last five years. Results: From September 2018 to September 2019, 96 comprehensive CRS checks were performed, with 29% of systems found to be installed correctly. Survey results showed a high level of reported confidence with CRS installation (N = 514, 70% female, 43% one child). Parents who had installed only one CRS reported higher confidence in their first install, compared to parents who had installed two or more systems. Conclusions: The error rate with CRS installation and use seen in London, Ontario and the region, is similar to that reported in previous research. Survey results showed high levels of self-reported confidence in CRS use, especially for parents who have installed only one CRS. There presents a need to better understand the root cause of the discrepancy between level of confidence and proper CRS use and to expand our understanding of CRS knowledge retention and transferability to subsequent systems.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".