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Record W1970492534 · doi:10.1080/14999013.2011.625591

International Trends in Demand for Forensic Mental Health Services

2011· article· en· W1970492534 on OpenAlexaffabout
Erika M. Jansman-Hart, Michael C. Seto, Anne G. Crocker, Tonia L. Nicholls, Gilles Côté

Bibliographic record

VenueInternational Journal of Forensic Mental Health · 2011
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversité du Québec à Trois-RivièresInstitut national de psychiatrie légale Philippe-PinelUniversité du Québec à MontréalBC Mental Health & Substance Use ServicesUniversity of British ColumbiaMcGill UniversityDouglas Mental Health University InstituteRoyal Ottawa Mental Health Centre
Fundersnot available
KeywordsForensic scienceMental healthPsychologyPsychiatryForensic psychiatryCriminologyGeographyArchaeology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.551
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.045
GPT teacher head0.384
Teacher spread0.339 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations111
Published2011
Admission routes2
Has abstractyes

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