Discrimination and Calibration Properties of the Level of Service Inventory–Ontario Revision in a Correctional Mental Health Sample
Bibliographic record
Abstract
We examined the predictive properties of the Level of Service Inventory–Ontario Revision (LSI-OR) in a sample of 604 provincially incarcerated men with mental illness from a correctional mental health facility followed up nearly 2 years after release. Recidivism base rates and LSI-OR scores were relatively consistent across major mental disorder categories, but higher among individuals with personality disorder, substance use disorder, or dual diagnosis. LSI-OR scores predicted general and violent recidivism in the overall sample and among specific diagnostic groups. Calibration analyses were conducted to model 1-year recidivism estimates for the overall sample and among individual diagnostic groups associated with individual LSI-OR scores. Good correspondence was observed among the different diagnostic groups, with some difference in recidivism trajectories given the differences in base rate. The results support the predictive properties of the LSI-OR with correctional mental health samples and inform the recidivism estimates associated with LSI-OR scores in this population.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".