Injury prevention/child passenger safety: factors influencing parents' correct and consistent use of booster seats
Bibliographic record
Abstract
School aged children in Canada are ten times more likely than any other age groups to experience death or severe injury in road crashes. Misuse of safety seats and lack of booster seat use are primarily responsible for these high rates of automobile death and injury. National surveys of booster seat use have noted regional differences; however, research on influences for usage is limited. This study examined influences on parents decision making for booster seat use in two Canadian provinces; British Columbia and Manitoba. British Columbia has recently legislated use of booster seats while Manitoba has not. Although parents in both provinces did not differ on the perceived safety benefits of using booster seats they did significantly differ on their intent to use them. Parents in British Columbia were significantly more likely to report they would always make sure their child rode in a booster seat. Parents perception of barriers to using booster seats did not differ significantly with one exception; Manitoba parents were more likely to perceive that children were teased for riding in booster seats. British Columbian parents reported significantly higher exposure to multi media messaging about booster seats and knew booster seats were legally required for children under 9 years of age. In contrast, Manitoba parents were confused regarding whether their province had booster seat legislation and had less exposure to messaging about booster seat safety. The results suggest that legislation and knowledge of the legislation for booster seat use may impact usage within Canada.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".