Does Clozapine Promote Employability and Reduce Offending among Mentally Disordered Offenders?
Bibliographic record
Abstract
OBJECTIVE: To compare employment pay, count of infractions, and clinical symptoms in psychiatric inmates treated with clozapine or other antipsychotics after 6 months of treatment. METHODS: Clinical charts and institutional offence records of psychiatric inmates (n = 98), comprised of those on clozapine (n = 65) and on other antipsychotics (n = 33), were reviewed at baseline and after 6 months of treatment. The outcome measures used were Brief Psychiatric Rating Scale (BPRS) scores, employment pay, medication compliance, and the frequency of institutional offences. A binary logistic regression model was used to analyze a categorical change in pay variable, while a negative binomial model was used to analyze the frequency of infractions. RESULTS: Treatment with clozapine was associated with greater odds of a pay increase (OR = 3.13; 95% CI 1.3 to 7.53, P = 0.01). However, patients on other antipsychotics had a more favourable improvement in BPRS (F = 5.44, df = 1,57, P = 0.02). Patients on other antipsychotics also had a higher count of posttreatment offences (Incidence Rate Ratio = 2.22; 95% CI 1.11 to 4.41, P = 0.02). CONCLUSION: Clozapine probably has a favourable effect on inmate behaviour and institutional adjustment. This effect can last up to 36 months after the initial dose.
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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 | 0.000 | 0.003 |
| 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) | 0.001 | 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".