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 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) | 0.000 | 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".