High peak drinking levels mediate the relation between impulsive personality and injury risk in emerging adults
Bibliographic record
Abstract
BACKGROUND: Alcohol-induced injury is one of the leading causes of preventable morbidity and mortality. We investigated the relationship between impulsive personality and physical injury (e.g. falls, sports), and whether peak drinking quantity specifically, and/or risky behaviour more generally, mediates the relationship between impulsivity and injury in undergraduates. METHOD: We used data from the winter 2021 UniVenture survey with 1316 first- and second-year undergraduate students aged 18-25 years (79.5% female) from five Canadian Universities. Students completed an online survey regarding their demographics, personality, alcohol use, risky behaviours, and injury experiences. Impulsivity was measured with the substance use risk profile scale, past 30-day peak alcohol use with the quantity-frequency-peak Alcohol Use Index, general risky behaviour with the risky behaviour questionnaire, and past 6-month injury experience with the World Health Organization's (2017) injury measurement questionnaire. RESULTS: Of 1316 total participants, 12.9% (n = 170) reported having sustained a physical injury in the past 6 months. Mean impulsivity, peak drinking quantity, and risky behaviour scores were significantly higher among those who reported vs. did not report injury. Impulsivity and peak drinking quantity, but not general risky behaviour, predicted injury in a multi-level generalized mixed model. Mediation analyses supported impulsivity as both a direct predictor of physical injury and an indirect predictor through increased peak drinking (both p < .05), but not through general risky behaviour. CONCLUSION: Results imply emerging adults with impulsive tendencies should be identified for selective injury prevention programs and suggest targeting their heavy drinking to decrease their risk for physical injury.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".