Evaluation of a youth unsafe driving video: a comparison of two communities
Bibliographic record
Abstract
Objectives To evaluate and compare the effectiveness of an injury prevention video (iDrive2) designed to raise awareness of youth about the risks and consequences of aggressive, unsafe driving in two Canadian communities, with different injury experiences. Methods The video with accompanying presentation was delivered to two high schools in different communities. A survey was designed and distributed to students to evaluate program effectiveness. Program components were scored on Likert-scales, with open-ended questions included. Ç2 and t tests were used to compare groups. Results There was a total of 651 completed surveys (462 (71%) Brantford; 189 (29%) London)). While <1/3 of each school responded that this was new information, the majority of students (91% Brantford; 83% London; p<0.001) found the program effective in raising awareness of unsafe driving, rating it 5 (London) and 6 (Brantford) out of 7 (p<0.001). The Brantford students were more likely to find the video effective to educate on driving distraction, speeding, drugs and buckling up (p<0.001). More Brantford students reported to better understand risks and learnt strategies (87% vs 69%; 87% vs 70%; p<0.001) and nearly all (97% vs 88%) would recommend the video. Conclusion While the majority of students found this program effective in raising awareness of unsafe driving (distractions, drugs, speeding, no seatbelt use), the results from Brantford group were more favourable. This high school had a fatal crash involving students in the months preceding the program. The context of this experience created a learning opportunity when students are more receptive, thereby maximising program effectiveness.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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 | 0.001 |
| 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".