Examining Factors in the Research Institute on Addictions Self-Inventory (RIASI): Associations with Alcohol Use and Problems at Assessment and Follow-Up
Bibliographic record
Abstract
Impaired driving is a leading cause of alcohol-related deaths and injuries. Rehabilitation or remedial programs, involving assessment and screening of convicted impaired drivers to determine problem severity and appropriate programs, are an important component of society's response to this problem. Ontario's remedial program, Back on Track (BOT), involves an assessment process that includes administration of the Research Institute on Addictions Self-Inventory (RIASI) to determine assignment to an education or treatment program. The purpose of this study is to identify factors within the RIASI and examine how factor scores are associated with alcohol use and problem indicators at assessment and six-month follow-up. The sample included 22,298 individuals who completed BOT from 2000 to 2005. Principal component factor analysis with varimax rotation was conducted on RIASI data and an eight factor solution was retained: (1) Negative Affect, (2) Sensation Seeking, (3) Alcohol-Quantity, (4) Social Conformity, (5) High Risk Lifestyle, (6) Alcohol Problems, (7) Interpersonal Competence, and (8) Family History. Regression analyses were conducted to examine associations between factors and alcohol and problem measures obtained at assessment and at follow-up. Most factors, except for Interpersonal Competence, were associated with more alcohol use and problems at assessment. A similar pattern was observed at 6-month follow-up, but interestingly some factors (Negative Affect, Sensation Seeking, Alcohol-Quantity and Family History) predicted fewer days of alcohol use. The Interpersonal Competence factor was associated with significantly lower levels of alcohol use and problems at both assessment and follow-up. This work suggests that the RIASI provides information on several domains that have important relationships with alcohol problem severity and outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".