Relationship of latent profiles from the RIA self inventory with various outcomes
Bibliographic record
Abstract
Recently Robert Mann and his colleagues, using a Canadian sample, performed a factor analysis on the RIA Self Inventory (RIASI), a screening instrument developed for use with DUI offenders. Results showed differential relationships of the eight identified factors with various outcomes. The current study was designed to confirm the factor structure of the RIASI in a sample from the United States and then to assess whether specific profiles could be identified that would help in development of intervention strategies. Subjects were referred to the Research Institute on Addictions (RIA) for clinical evaluation. As part of that process, the DUI offenders were extended an offer to participate in this research project. Of the 765 individuals referred to the RIA from various courts in the Western New York area, 549 agreed to participate in the study, with 520 having valid data. The assessment included alcohol and other drug use and problems, abstinence self-efficacy, psychiatric distress, hostility, family history, readiness to change, and the RIASI. An 18-month follow-up was also conducted, with driver records obtained. Confirmatory factor analysis on the RIASI showed a relatively good fit. Indications for the latent class analyses based on the sub-dimensions of the RIASI indicated the most optimum solution was for 4 classes. There were significant associations of the profiles with alcohol problems, drug problems, alcohol expectancies, abstinence self-efficacy, psychiatric distress, hostility, and treatment entry but not for recidivism. The results indicate that the RIASI has reliable underlying dimensions that can be used to identify subgroups of offenders. Differences in the subgroups can lead to more effective intervention development and use.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".