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Record W2043611299 · doi:10.1016/s0924-9338(13)76098-x

950 – Achieving Positive Outcome - Reducing Recidivism Within Toronto's Mental Health Court Support Program: The Mount Sinai Hospital Experience

2013· article· en· W2043611299 on OpenAlexaffabout
W. K. Chow, Carmen Tse, Samuel Law, Helen F.K. Chiu, Molyn Leszcz, Joel Sadavoy

Bibliographic record

VenueEuropean Psychiatry · 2013
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsMount Sinai HospitalUniversity of Toronto
Fundersnot available
KeywordsRecidivismEthnic groupDemographyCriminal justiceMental healthPsychiatryMedicinePsychologyCriminologySociologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

This paper investigates the recidivism of Mount Sinai Hospital mental health court support program in Toronto, Canada among patients involved in the criminal justice system. It also looks to find relationships between recidivism and factors including gender, age and ethnicity. Method Follow up periods of up to 48 months after the time of initial admission to the program was conducted and the frequency of re-offense was observed. Comparisons for the significance of risk factors were analyzed using t-tests and Chisquare tests. Results 191 clients were admitted to the Mount Sinai Hospital Court Support Program between September 2001 and June 2007. At first admission, the mean ± s.d. age was 35.8 ± 9.8 years (range=18-74 years; n=184). The median age was 35 years. The modal age was 34 years. Of the 191 clients, 16 (8.4%) reoffended. Two of them (12.5%) had a third offense; and 1 (6.3%) had a total of four offenses within this tracking period. it appears that re-offense is more likely between 13 and 24 months. No re-offense was noted beyond the 48 months. The gender distribution was not significantly different between reoffenders and non-reoffenders. The mean age at first admission also did not differ between reoffenders and non-reoffenders. The distribution of ethnic groups among reoffenders and non-reoffenders did not differ. Conclusions The findings seem to indicate that recidivism has no relationship with gender, age and ethnic groups. The comprehensive and length of support services seem more important in preventing 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)0.002
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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.737
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.020
GPT teacher head0.331
Teacher spread0.311 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2013
Admission routes2
Has abstractyes

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