The development and preliminary evaluation of an education intervention to prevent driveway run-over incidents
Bibliographic record
Abstract
Objective: While driveway run-over incidents continue to be a cause of serious injury and deaths among young children in Australia, few empirically evaluated educational interventions have been developed which address these incidents. Addressing this gap, this study describes the development and evaluation of a paper-based driveway safety intervention targeting caregivers of children aged 5 years or younger. Design: Cross-sectional survey. Method and setting: Informed by previous research, the intervention targeted key caregiver safety behaviours that address driveway risks. To assess the impact of the intervention, 137 Queensland (Australia) caregivers (95.0% women; mean age = 34.97 years) were recruited. After receiving the intervention, changes to a number of outcomes such as caregiver risk perception, safety knowledge and behavioural intentions were measured. Results: Findings indicated that the intervention had increased general and specific situational risk awareness and safety knowledge among a substantial proportion of participants. Close to one-quarter of the sample strongly agreed that the intervention had increased these outcomes. In addition, 71.6% of the sample reported that they intended to make changes to their routine in and around the driveway, as a result of reading the intervention material and a further, quarter of the participants strongly agreed that the information provided would be a help both to themselves (26.5%) and other caregivers (33.8%) to keep their children safe in the driveway. Conclusion: While the educational intervention requires further validation, findings from this study suggest that intervention content and format increases driveway safety.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".