International Trends in Demand for Forensic Mental Health Services
Bibliographic record
Abstract
Deinstitutionalization, changes in both criminal and civil law, and the use of the criminal justice system to manage problematic behavior by individuals with serious mental illnesses have affected the number of people entering forensic mental health systems. This review examines data revealing trends in demand for forensic services in Canada, the United States, and internationally. The number of beds and resources allocated to forensic mental health services has steadily increased not only in Canada, but in other countries across the world. In Canada, the number of new accused entering the system doubled annually from 1992 to 2004. In the United States, the number of individuals found not guilty by reason of insanity (NGRI), forensic beds and forensic expenditures have all increased dramatically in recent years. Furthermore, countries in Western Europe – Austria, Denmark, England, Germany, Ireland, Italy, the Netherlands, Spain and Switzerland – have reported an increase of 110%, on average, in the number of forensic beds between 1990 and 2006. These increases have implications for both forensic and non-forensic mental health services. Suggestions to reduce forensic demand and increase the successful reintegration of forensic clients into the community are discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".