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Record W4415242910 · doi:10.1080/14789949.2025.2576176

A qualitative and observational retrospective analysis of community treatment orders issued in Quebec for the year 2022

2025· article· en· W4415242910 on OpenAlexaffabout
Alexandre Hudon, Ann-Julie Huberdeau, Anne-Renée Courtemanche, Jeanne-Marie Allard, Jean-Sébastien Sauvé, Stéphanie Borduas Pagé

Bibliographic record

VenueJournal of Forensic Psychiatry and Psychology · 2025
Typearticle
Languageen
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalUniversité de MontréalInstitut Universitaire en Santé Mentale de QuébecInstitut national de psychiatrie légale Philippe-Pinel
Fundersnot available
KeywordsObservational studyQualitative researchAgency (philosophy)Retrospective cohort studyWork (physics)

Abstract

fetched live from OpenAlex

This study provides an observational analysis of community treatment orders (CTOs) in Quebec for 2022, utilizing a qualitative grounded theory approach and descriptive statistics from judgments in the Superior Court of Quebec’s public database, Société québécoise d’information juridique (SOQUIJ), from January to December 2022. Excluding residential placement orders without pharmacological treatment, 209 judgments were analyzed, focusing on sociodemographic traits, court information, clinical data, and treatment details. A significant portion of CTOs proposed by medical teams was granted (202 out of 209, or 97.6%), with 31 receiving partial approval. The study covered 124 male and 85 female patients, aged 19–76 and 14–94, respectively, with average ages of 41 and 53. Roughly a third were diagnosed with psychotic disorders, alongside other conditions like bipolar affective disorders, delusional disorder, neurocognitive disorders, and substance use. The initial CTO duration averaged 2.53 years, with rehospitalization ranging from 7 to 120 days. This analysis highlights Quebec’s use of CTOs, suggesting further research on their efficacy in supporting patient management of illnesses.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.475
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.095
GPT teacher head0.488
Teacher spread0.393 · 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 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

Citations0
Published2025
Admission routes2
Has abstractyes

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