Ethnicity, Immigration Status, And Driving Under The Influence Of Alcohol
Bibliographic record
Abstract
BACKGROUND: Understanding risk factors for driving under the influence of alcohol (DUIA) informs development of effective interventions. This study examined the association between ethnicity, immigration status, and DUIA, exploring psychological distress and hazardous drinking as additional contributors. METHOD: Data were derived from the 2003-2011 cycles of the Centre for Addiction and Mental Health (CAMH) Monitor of 16,101 adults from Ontario, Canada. Hierarchical binary logistic regression analysis assessed self-identified ethnicity and immigration status as predictors of DUIA, adjusting for sociodemographics and driving exposure (Model 1), psychological distress (Model 2), and hazardous drinking (Model 3). RESULTS: In Model 1, respondents born outside of Canada had reduced odds of engaging in DUIA compared to those born in Canada (AOR = 0.72, 95%CI = 0.56 - 0.92). Relative to those identifying as Canadian, the odds of DUIA were significantly reduced for those identifying as East Asian (AOR = 0.28, 95%CI = 0.13 - 0.61) and South Asian (AOR = 0.52, 95%CI = 0.27 - 0.98). In Model 3, individuals who reported psychological distress (AOR = 1.69, 95%CI = 1.33 - 2.16) and those who reported hazardous drinking (AOR = 6.28, 95%CI= 5.13 - 7.69) were more likely to DUIA. Those identifying as East Asian continued to have reduced odds of DUIA compared to those identifying as Canadian (AOR = 0.38, 95%CI = 0.17 - 0.85). CONCLUSION: Individuals born outside of Canada were less likely to engage in DUIA than individuals born in Canada. Drivers who self-identified as East Asian were less likely to DUIA than those who self-identified as Canadian. Understanding ethnic differences underlying divergent risks for DUIA will improve prevention initiatives and remedial measures programming.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".