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Record W2346668973 · doi:10.1002/bsl.2223

Housing Trajectories of Forensic Psychiatric Patients

2016· article· en· W2346668973 on OpenAlexaff
Leila Salem, Anne G. Crocker, Yanick Charette, Christopher M. Earls, Tonia L. Nicholls, Michael C. Seto

Bibliographic record

VenueBehavioral Sciences & the Law · 2016
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsRoyal Ottawa Mental Health CentreUniversité de MontréalBC Mental Health & Substance Use ServicesUniversity of British ColumbiaMcGill UniversityDouglas Mental Health University Institute
Fundersnot available
KeywordsDispositionSupportive housingMental illnessPsychiatryPopulationSuicide preventionPsychologyHuman factors and ergonomicsPoison controlMedicineMental healthEnvironmental healthSocial psychology

Abstract

fetched live from OpenAlex

The objectives of this study were to describe the disposition and housing trajectories of individuals found Not Criminally Responsible on account of Mental Disorder (NCRMD), and the factors that predict different trajectories. To do so, disposition and housing status were coded for 934 NCRMD patients over a 36-month follow-up period. Sequential data analysis resulted in four distinct trajectories: detention in hospital, conditional discharge in supportive housing, conditional discharge in independent housing, and absolute discharge to unknown housing. The likelihood of a placement in supportive housing compared with detention significantly decreased for individuals with a higher index offense severity. Less restrictive trajectories were significantly predicted by clinical factors. The results revealed little change in the disposition and housing trajectories of NCRMD patients. Furthermore, decisions about disposition and housing placement reflect a knowledge-practice gap between risk factors known to be predictive of community resources use in the forensic population. Copyright © 2016 John Wiley & Sons, Ltd.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.094
GPT teacher head0.430
Teacher spread0.335 · 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

Citations13
Published2016
Admission routes1
Has abstractyes

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