Effectiveness of a remedial program for impaired driving offenders delivered in-person versus via videoconferencing: A randomized controlled trial
Bibliographic record
Abstract
Background Remedial education programs are effective at reducing impaired driving recidivism. The Back on Track (BOT) program in Ontario, Canada, was mandated to be delivered in-person; however, the COVID-19 pandemic necessitated a shift to online delivery. Existing literature suggests that substance use treatment delivered via videoconferencing is as effective as treatment delivered in-person; however, little is known about the comparative effectiveness of these delivery modes for impaired driving education. Purpose Conducted a randomized controlled trial examining effectiveness of in-person versus online delivery of BOT. Method 145 participants attending the 8-hour BOT workshop were randomly assigned to in-person (n = 71) or videoconference (n = 75) modalities. Assessments were collected immediately, 6 months, and 9–12 months following workshop participation. Results Immediately following the workshop, participants in both conditions demonstrated positive change on a 5-point scale measuring negative affect, attitudes, and behavioural intentions related to impaired driving. Mean increase from baseline ranged from 0.13 to 0.48 for the in-person and 0.05 to 0.12 for the videoconference conditions. Participants in both in-person and videoconference conditions demonstrated high mean scores on client satisfaction (64 out of 70 for both groups), clarity of presentation (108 and 107 out of 110, respectively), and learner engagement (123 and 124 out of 135, respectively). Positive mean changes from baseline in number of days consuming alcohol and tobacco were seen 9–12 months following workshop participation in both in-person (0.9 and 14.3) and videoconference conditions (3.3 and 10.4). Few differences between conditions were identified. Conclusion Findings provide support for continued online BOT program delivery.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".