Substance Use and Related Harms Among Adolescents With and Without Traumatic Brain Injury
Bibliographic record
Abstract
OBJECTIVE: The relationship between self-reported lifetime traumatic brain injury (TBI) and drug and alcohol use and associated harms was examined using an epidemiological sample of Canadian adolescents. SETTINGS AND DESIGN: Data were derived from a 2011 population-based cross-sectional school survey, which included 6383 Ontario 9th-12th graders who self-completed anonymous self-administered questionnaires in classrooms. Traumatic brain injury was defined as loss of consciousness for at least 5 minutes or a minimum 1-night hospital stay due to symptoms. RESULTS: Relative to high schoolers without a history of TBI, those who acknowledged having a TBI in their lifetime had odds 2 times greater for binge drinking (5+ drinks per occasion in the past 4 weeks), 2.5 times greater for daily cigarette smoking, 2.9 times greater for nonmedical use of prescription drugs, and 2.7 times greater for consuming illegal drug in the past 12 months. Adolescents with a history of TBI had greater odds for experiencing hazardous/harmful drinking (adjusted odds ratio [aOR] = 2.3), cannabis problems (aOR = 2.4), and drug problems (aOR = 2.1), compared with adolescents who were never injured. CONCLUSION: There are strong and demographically stable associations between TBI and substance use. These associations may not only increase the odds of injury but impair the quality of postinjury recovery.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".