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Record W3117348920 · doi:10.15173/ijrr.v3i2.4112

Review on the effectiveness of Canadian and American mental health courts

2020· article· en· W3117348920 on OpenAlexaffabout
David Tyler Dunford, Andrew M. Haag

Bibliographic record

VenueInternational Journal of Risk and Recovery · 2020
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversity of AlbertaAlberta Health Services
Fundersnot available
KeywordsRecidivismPsycINFOMental healthPsychologyPsychiatryMental illnessEconomic JusticeCriminal justiceCriminologyClinical psychologyPolitical scienceMEDLINELaw

Abstract

fetched live from OpenAlex

Objective: This systematic review synthesizes mental health court (MHC) research across the United States and Canada. This study reviews and compares the operations and practices of MHCs across both countries, as well as their recidivism rates. Methods: We gathered from existing literature to present common MHC practices used across the United States. However, in response to the lack of literature about Canadian day-to-day practices, we developed a questionnaire and contacted every Canadian MHC. In total, we contacted 36 Canadian MHCs, and 19 courts filled out a questionnaire. With respect to recidivism rates, we conducted a comprehensive literature search in February and March 2019 in PsycINFO, Google Scholar, Web of Science, and National Criminal Justice Reference Service Abstracts using the keywords mental health court, therapeutic justice, serious mental illness, mentally ill offenders, mental health diversion and problem-solving courts. Results: Canadian and American MHCs have similar practices. However, American MHC’s have more robust screening measures and typically admit more participants with schizophrenia, bipolar disorder, and major depressive disorder into their programs compared to Canadian MHCs. MHC participants in both countries typically had lower recidivism rates compared to regular docket court participants. Conclusions: MHC research should inform public policy. Additional research should move in the direction of discovering the predictors for why MHCs reduce recidivism.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.467
Threshold uncertainty score0.936

Codex and Gemma teacher scores by category

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

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 head0.021
GPT teacher head0.316
Teacher spread0.295 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations16
Published2020
Admission routes2
Has abstractyes

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