Assessing the Youth Safety Lab Program Through a Health Equity Lens
Bibliographic record
Abstract
Abstract Background Injury is the leading cause of death among youth in Canada. This study aimed to evaluate the effectiveness of the Youth Safety Lab (YSL) program in improving injury prevention knowledge and skills among high school students in the Greater Toronto Area, with attention to equity across sociodemographic groups. Methods A retrospective longitudinal study involved 1,805 students from over 50 high schools at three time points: before the program, immediately after, and three months later. The survey, based on the Health Action Process Approach (HAPA), assessed five aspects related to injury prevention. Wilcoxon signed-rank tests and regression analyses were used to examine changes and disparities in outcomes. Results Scores across all five sections showed significant improvement following the intervention, with the greatest gains observed in bleeding injury response. Black students were underrepresented in follow-up participation, and female students exhibited greater improvements than male students in bleeding injury response, indicating potential disparities in engagement and outcomes. Conclusions The YSL program effectively enhances injury prevention awareness among high school students in the GTA. However, disparities in participation and outcomes highlight the need for more inclusive and responsive injury prevention programs. What is already known on this topic The P.A.R.T.Y. (Prevent Alcohol and Risk-Related Trauma in Youth) program has been demonstrated to effectively improve students’ risk awareness. Injury risk and its impact vary unequally among different population groups. What this study adds The YSL program demonstrated notable improvements in injury prevention knowledge and skills, especially in bleeding injury response. Disparities in access to and outcomes of health education interventions persist among diverse demographic groups. How might this study affect research, practice, or policy Reinforces the value of injury prevention programs like the YSL and emphasizes the need to explore sustainable models for access, funding, and scalability to better reach priority populations. Highlights the need to design more inclusive and responsive injury prevention programs that address barriers to participation and ensure equitable benefits for diverse youth populations.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".