Driving While Impaired (DWI) by Alcohol Convictions Among Alcohol, Cocaine, and Cannabis Clients in Treatment
Bibliographic record
Abstract
OBJECTIVE: Although studies have demonstrated that clients in treatment for alcohol abuse are more at risk of driving while impaired (DWI) by alcohol than normal licensed drivers from the general population, no research was found on DWI convictions among those in treatment for abusing cannabis or cocaine. The purpose of this article is to compare DWI convictions among clients in treatment for alcohol, cannabis, cocaine, or various combinations of these substances, compared to a matched population control group. METHOD: A stratified random sample of driver records was drawn from seven client groups who sought treatment in 1994 for alcohol, cannabis, cocaine, or any combination of these substances (n = 445). A random sample of drivers, frequency matched by age and sex (n = 566), served as control subjects. RESULTS: Logistic regression analysis, controlling for sex and age, was conducted to assess whether DWI convictions were elevated for each of the client groups, compared to controls. Two sets of analyses were conducted, before treatment (from 1985 to 1993) and after treatment (from 1995 to 2000). In the time period before treatment, every drug group except the "cannabis only" group had significantly more DWI convictions than controls (p < .05). In the period after treatment, the "alcohol only," "cocaine only," "alcohol and cocaine," and the "cocaine and cannabis" groups still had significantly more DWI convictions than controls (p < .05). CONCLUSION: The results show that DWI convictions are elevated among those who abused cocaine but not among those who abused cannabis. The results suggest that cross-addiction of alcohol and cocaine is common, and problematic drinking among cocaine clients can go undetected when clients are being diagnosed for treatment.
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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 | 0.000 | 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) | 0.001 | 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".