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Record W7117876340 · doi:10.1136/ip-2025-045993

Assessing the Youth Safety Lab program through a health equity lens

2025· article· en· W7117876340 on OpenAlexaffabout
Qizhi Mao, Shaelyn Fitzpatrick, B Tanenbaum

Bibliographic record

VenueInjury Prevention · 2025
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsSunnybrook Health Science CentreWestern University
Fundersnot available
KeywordsOccupational safety and healthInjury preventionPoison controlSuicide preventionEquity (law)Human factors and ergonomicsLens (geology)

Abstract

fetched live from OpenAlex

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) programme in improving injury prevention knowledge and skills among high school students in the Greater Toronto Area (GTA), with attention to equity across sociodemographic groups. METHODS: A retrospective longitudinal study involved 1805 students from over 50 high schools at three time points: before the programme, immediately after and 3 months later. The survey, based on the Health Action Process Approach, 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 under-represented 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 programme 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 programmes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.950
Threshold uncertainty score0.486

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.107
GPT teacher head0.498
Teacher spread0.391 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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