Predictors of completion status in a remedial program for male convicted drinking drivers.
Bibliographic record
Abstract
OBJECTIVE: Rates of attrition in alcohol and drug treatment programs are often greater than 50%, and completion of treatment has been shown to be a potent predictor of posttreatment outcome. The current study examined both rates and predictors of completion among male participants in a remedial measures program for convicted drinking drivers. METHOD: Male individuals (n = 5,409) convicted of a drinking driving offense in Ontario between October 2000 and December 2002 who did and did not complete a mandatory rehabilitation program were described in terms of demographic, drug use and legal variables collected at time of assessment. RESULTS: The program completion rate was extremely high (97.3%). In multivariate analyses, noncompleters-relative to completers-were younger; drank more frequently; were less likely to own a home; and were more likely to live in urban centers, have two or more lifetime impaired driving convictions and have experienced more than one adverse consequence of substance use. CONCLUSIONS: Ontario's remedial measures program for convicted drinking drivers, in which the return of a suspended license after the period of mandatory suspension is contingent on the completion of the program, demonstrates a very low level of client attrition. Individuals who do not complete the program bear many similarities to those at high risk for persistent drink-drive behavior and its associated negative health consequences.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".