Gambling and Subsequent Road Traffic Injuries: A Longitudinal Cohort Analysis
Bibliographic record
Abstract
BACKGROUND: To compare the risks of a road traffic injury (RTI) crash among adults who were involved in high-risk gambling and those who did not gamble. METHODS: We conducted a linked longitudinal cohort analysis of adult persons in large population survey conducted during 2007 and 2008 in Ontario, Canada. We used responses to Problem Gambling Severity Index to distinguish persons as nongamblers, no-risk, low-risk, or high-risk gamblers. All persons were subsequently monitored for a subsequent RTI crash as a driver, pedestrian, or bicyclist up to March 31, 2014, through health insurance databases. We estimated relative risks as rate ratios (RRs) with 95% confidence intervals (95% CIs). RESULTS: In all, 30,652 adults were included, of whom 52% self-identified as gamblers, including 49% as no-risk gamblers, 2% as low-risk gamblers, and 1% as high-risk gamblers. During a median follow-up period of 6.8 years, 708 participants (2%) were involved in 821 RTI crashes. The absolute risks of an RTI were 6.4 per 1000 person-years (95% CI 3.7-10.4) in high-risk gamblers and 3.6 per 1000 person-years (95% CI 3.2-4.0) in nongamblers. The relative risks for RTI crashes were significantly higher in high-risk gamblers than in nongamblers (adjusted RR 1.68, 95% CI 1.03-2.76). The risks for RTI crashes as a driver were augmented in high-risk gamblers than in nongamblers (RR 1.97, 95% CI 1.13-3.43). CONCLUSIONS: We found an increased risk of an RTI crash among drivers who self-identified as high-risk gamblers. Further research exploring the underlying mechanisms of these associations might interest health professionals to monitor RTI risks in adults involved in high-risk gambling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".