Adolescents’ engagement in multiple risk behaviours is associated with concussion
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to investigate the relationship between engagement in multiple risk behaviours (MRB) and concussion amongst youth. METHODS: This was a cross-sectional study that used survey data collected from 3059 students in grades 6-10 (approximate ages 11-15 years) from Ontario, Canada. Students reported whether or not they had a medically diagnosed concussion within the previous 12 months and the frequency that they participated in several risky behaviours including fighting, bullying, smoking, drinking alcohol, using illicit drugs, drinking caffeinated beverages, not using protective equipment, and having unsafe sex. Responses to the risky behavior items were used to create a MRB score. The association between MRB and concussion was explored using logistic regression that controlled for several confounding variables. RESULTS: Approximately 10.7% of students reported that they had a medically diagnosed concussion within the past year. A dose-response relationship was found between MRB and concussion among students in grades 9-10, while in grades 6-8 students only those in the highest MRB quartile had an increased likelihood of concussion. The relative odds for concussion in the highest versus the lowest MRB quartile were 4.67 (95% confidence interval: 2.33, 9.35) in grades 9-10 students and 2.94 (95% confidence interval, 1.90, 4.56) in grades 6-8 students. CONCLUSIONS: Engagement in MRB may be an important etiologic component of adolescent concussion. Future studies should address whether behavioural interventions designed to decrease engagement in MRB reduce the risk of concussion and other injuries.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".