Adherence to Antipsychotic Medication and Criminal Recidivism in a Canadian Provincial Offender Population
Bibliographic record
Abstract
Preliminary evidence suggests that adherence to antipsychotic medication reduces criminal recidivism among patients diagnosed with schizophrenia. However, existing studies operationalize antipsychotic adherence as a binary variable (usually using a threshold of ≥80%), which does not reflect the prevalence of suboptimal adherence in real-world settings. The purpose of the current analysis was to investigate the association between successive ordinal levels of antipsychotic adherence and criminal recidivism in a well-defined sample of offenders diagnosed with schizophrenia (n = 11462). Adherence was measured using the medication possession ratio (MPR) and analyzed as a time-dependent covariate in multivariable regression models. Data were drawn from linked, comprehensive diagnostic, pharmacy and justice system records, and individuals were followed for an average of 10 years. Adjusted rate ratios (ARR) and confidence intervals (CI) are reported. Overall mean MPR was 0.41. Increasing levels of antipsychotic adherence were not associated with progressively lower rates of offending. However, when compared to the reference group (MPR ≥ 80%) all lower adherence levels were significantly associated (P < .001) with increased risk of violent (ARR = 1.58; 95% CI = 1.46-1.71) and nonviolent (ARR = 1.41; 95% CI = 1.33-1.50) offenses. Significance was replicated in separate sensitivity analyses. Previously published studies reporting reductions in crime may have been influenced by antipsychotic adherence ≥80%. Binary operationalization of adherence is an inaccurate predictor of recidivism. Future research addressing functional outcomes of antipsychotic adherence should conceptualize adherence as an incremental independent variable.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".