Psychological distress and collision involvement among adult drivers
Bibliographic record
Abstract
Abstract The current study examines the impact of psychological distress on the likelihood of reporting collision involvement in the preceding year. Two measures of psychological distress were obtained from the 12‐item General Health Questionnaire (GHQ‐12): depression–anxiety and social functioning. Data are based on the 2002–2004 Centre for Addiction and Mental Health Monitor (36 months), a repeated cross‐sectional telephone survey of Ontario adults aged 18 and older (n = 4935). Logistic regression analyses were performed on collision involvement within the past 12 months with the measures of depression–anxiety, social functioning and demographic factors as independent variables. The analyses revealed that the odds of involvement in a collision in the last 12 months were significantly related to the demographic factors of age, location of residence, income, educational level and marital status. After controlling for demographic factors, the odds of collision involvement increased significantly as the depression–anxiety score increased (odds ratio = 1.05 for each unit increase). These results suggest that higher levels of psychological distress, as indicated by scores on the depression–anxiety scale of the GHQ‐12, are associated with higher likelihood of collision involvement in the previous year. Research to understand the link observed here between distress and collision risk in the general population is needed. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".