Examining Predictive Validity of the Level of Service Inventory–Ontario Revision (LSI-OR) Substance Abuse Subscale for Different Types of Substance Users
Bibliographic record
Abstract
Substance abuse is a risk factor for recidivism that is commonly assessed by the Level of Service Inventory–Ontario Revision (LSI-OR) via the Substance Abuse subscale. Research has yet to examine the predictive validity of this subscale relative to types of substances abused. To explore this, substance abuse history, LSI-OR information, and recidivism were coded for a sample of 498 individuals convicted of a crime with a current substance abuse problem. These individuals were classified by the types and number of substances abused. Results of this study provide some evidence supporting the predictive validity of the LSI-OR Substance Abuse subscale. Furthermore, we found preliminary evidence supporting the predictive validity of the subscale for substance abusers relative to types of substances abused and for those who abuse a single substance versus multiple substances. These results have implications for research, policy, and correctional practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".