A Randomized Controlled Trial of Brief Motivational Interviewing in Impaired Driving Recidivists: A 5‐Year Follow‐Up of Traffic Offenses and Crashes
Bibliographic record
Abstract
BACKGROUND: In a previously published randomized controlled trial (Brown et al. Alcohol Clin Exp Res 2010; 34, 292-301), our research team showed that a 30-minute brief motivational interviewing (BMI) session was more effective in reducing percentages of risky drinking days in drunk driving recidivists than a control information-advice intervention at 12-month follow-up. In this sequel to the initial study, 2 main hypotheses were tested: (i) exposure to BMI increases the time to further arrests and crashes compared with exposure to the control intervention (CTL) and (ii) characteristics, such as age, moderate the benefit of BMI. METHODS: A sample of 180 community-recruited recidivists who had drinking problems participated in the study. Participants gave access to their provincial driving records at baseline and were followed up for a mean of 1,684.5 days (SD = 155.7) after randomization to a 30-minute BMI or CTL session. Measured outcomes were driving arrests followed by convictions including driving while impaired (DWI), speeding, or other moving violations as well as crashes. Age, readiness to change alcohol consumption, alcohol misuse severity, and number of previous DWI convictions were included as potential moderators of the effect of the interventions. RESULTS: For arrests, Cox proportional hazards modeling revealed no significant differences between the BMI and the CTL group. When analyses were adjusted to age tertile categories, a significant effect of BMI in the youngest age tertile (<43 years old) emerged. For crashes, no between-group differences were detected. CONCLUSIONS: BMI was better at delaying DWI and other dangerous traffic violations in at-risk younger drivers compared with a CTL similar to that provided in many remedial programs. BMI may be useful as an opportunistic intervention for DWI recidivism prevention in settings such as DWI courts. Treatment effectiveness studies are needed to ascertain how the present findings generalize to the real-world conditions of mandated relicensing programs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".