THE RELATIONSHIPS AMONG MENTAL HEALTH, MEDICINAL DRUGS, DRINKING AND DRIVING AND ROAD RAGE AND MOTOR VEHICLE COLLISIONS ON A REPRESENTATIVE SAMPLE OF ONTARIO ADULTS
Bibliographic record
Abstract
Studies have demonstrated the relationships between motor vehicle collisions and anxiety and/or mood disorders, antidepressant and anxiolytic medication use, drinking and driving, and road rage. It is unclear if symptoms of anxiety and/or mood disorders are directly associated with motor vehicle collisions or if other factors mediate the effect. This thesis examines the effects of psychiatric distress, medication use, drinking and driving, and road rage on motor vehicle collisions. Cross-sectional data from the Centre for Addiction and Mental Health Monitor were used in a hierarchical logistic regression analysis. Demographic predictors, psychiatric distress, and mediating variables were entered in blocks into five models. Findings indicated the relationship between psychiatric distress and motor vehicle collisions was not mediated by antidepressant and/or anxiolytics; however, it was mediated by drinking driving and road rage. The cross-sectional data make the causal nature of these relationships unclear and further research is needed.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".