Predictive Validity of the RIASI: Alcohol and Drug Use and Problems Six Months Following Remedial Program Participation
Bibliographic record
Abstract
The ability of screening instruments for convicted drinking drivers to predict subsequent alcohol and drug-related problems rarely has been studied. The predictive validity of the Research Institute on Addictions Self-Inventory (RIASI) was investigated in a sample of 6,003 convicted drinking drivers who were participating in Back on Track (BOT), Ontario's remedial measures program for convicted drinking drivers. All BOT participants complete an assessment (which includes the RIASI), followed by a brief education or treatment program, and concluded 6 months later by a follow-up interview. The follow-up interview collects information on self-reported alcohol and other drug use and problems, and contacts with other health care providers in the 90 days prior to the follow-up contact. The ability of scores on the RIASI to predict these measures was assessed. The results revealed that, for almost all comparisons, individuals who used alcohol and other drugs, reported more substance-related problems at follow-up, and reported more contacts with other health and addictions providers had significantly higher scores on the RIASI total score and the RIASI recidivism scale at the initial assessment. The data indicate that this instrument appears to be able to identify individuals who will experience alcohol and drug related problems in the future.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".