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Record W4296041393 · doi:10.35502/jcswb.241

Cost analysis of the Saskatoon Mental Health Strategy (MHS) court

2022· article· en· W4296041393 on OpenAlexafffundvenueabout
Alexandra M. Zidenberg, Ashmini G. Kerodal, Lisa M. Jewell, Glen Luther

Bibliographic record

VenueJournal of Community Safety and Well-Being · 2022
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversity of SaskatchewanThe King's University
FundersUniversities Space Research AssociationUniversity of Saskatchewan
KeywordsMental healthEconomic JusticeCriminal justicePopulationBusinessCriminologyActuarial sciencePolitical sciencePsychologyPsychiatryEnvironmental healthLawMedicine

Abstract

fetched live from OpenAlex

Housing inmates, particularly those living with mental health concerns, is a very expensive prospect. Mental health courts (MHCs) are designed to divert justice-involved individuals living with mental health concerns away from the traditional criminal justice system and to mitigate some of the issues commonly seen in these systems. Given this diversion, it would seem that MHCs could reduce costs associated with crimes committed by this population. While intuitive, these cost savings are an untested assumption as there has been very little research examining the costs of these programs, particularly in Canada. Thus, this study presents the findings from a cost analysis of the Saskatoon Mental Health Strategy Court in Saskatchewan, Canada. Results demonstrated that Court costs increased in the first and second year post-Court entry. Most concerningly, a large proportion of these increased costs seem to be attributable to administrative charges applied by the Court. Recommendations for MHC operation and potential impacts of the cost analysis are further explored.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.439
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.031
GPT teacher head0.331
Teacher spread0.301 · 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 designObservational
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

Citations2
Published2022
Admission routes4
Has abstractyes

Explore more

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