Influence of Mild Traumatic Brain Injury History and Mental Health Status on Alcohol and Cannabis Use in University Athletes
Bibliographic record
Abstract
OBJECTIVE: This study examined the relationship between mild traumatic brain injury (mTBI) history, mental health, and sex with single and polysubstance use in university athletes. DESIGN: Observational study. SETTING: University in Ontario, Canada. PARTICIPANTS: Participants were identified from a dataset of 416 university athletes ages 18 to 21. Participants were classified based on their substance use habits and, 153 met criteria for the nonsubstance group, 195 for the alcohol use (AU) only group, and 64 polysubstance use group [ie, a combined substance use (AU+) group]. INDEPENDENT VARIABLES: Athletes received baseline assessments and completed self-reported questions regarding alcohol, cannabis, or other recreational substance use, the Patient Health Questionnaire-9, self-reported mTBI history, and self-reported anxiety, and/or panic disorder endorsement information. MAIN OUTCOME MEASURES: Comparison of mTBI history and mental health status between individuals in the alcohol only or polysubstance use group. RESULTS: Mild traumatic brain injury history was a significant predictor of AU ( P < 0.001) and AU+ ( P < 0.001). Anxiety endorsement was also a significant predictor of polysubstance use ( P < 0.001) and there was a small but nonsignificant association of polysubstance use in men ( P = 0.057). CONCLUSIONS: University athletes who experience mTBI are more likely to engage in single or polysubstance use and athletes who experience anxiety are more likely to engage in polysubstance use. Consideration of mTBI history and mental health may inform clinical concussion management for identifying potential high-risk behavior such as polysubstance use in university athletes and tailoring intervention strategies (eg, incorporating education about substance use).
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".