Offenders with Mental Disorder on Five Continents: A Comparison of Approaches to Treatment and Demographic Factors Relevant to Measurement of Outcome
Bibliographic record
Abstract
Specialist forensic mental health service development continues worldwide. Given their generally small size and slow patient turnover, aggregating multi-site data could aid in the study of their effectiveness, safety, and value for money. The study compares such context of care and treatment philosophies in nine countries. National databases on demographics, mental disorders, and offending were identified. Participating forensic mental health practitioners independently rated likely outcomes for standard cases of serious offenders with psychosis or personality disorder. Gender distribution was similar between populations, but there were differences in age distribution and proportions of ethnic groups. Rates of psychosis were similar, but there were considerable population-based differences in substance misuse disorder rates, other substance misuse indicators and in criminal conviction statistics. Case analysis confirmed shared preferences for mental health disposals for people with psychosis, and penal disposals otherwise, with differences only in process details. Criminal recidivism was thus found to be a poor comparative measure between these countries, as it was impossible to adjust fully for differences in crime classification and measurement. Clinical outcome measures may be less vulnerable to national differences, but prevalence and type of substance misuse must be rated precisely when sharing or comparing service outcome data between nations.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".