Community Treatment Orders use Among Persons With a First Episode of Psychosis in Quebec
Bibliographic record
Abstract
OBJECTIVES: Specialized early intervention for psychosis can reduce the duration of untreated psychosis and improve clinical and functional outcomes. However, poor adherence to treatment is frequent. The literature on community treatment orders (CTOs) use in first-episode psychosis (FEP) as a means to improve treatment adherence is limited. In the context of early intervention for psychosis services (EIS), this study aims to describe (1) the frequency of CTOs utilisation, (2) the trend of CTOs use over time, (3) the timing and reasons for requesting CTOs and (4) the baseline characteristics of FEP patients on CTOs compared to those who were not. METHOD: A 5-year prospective longitudinal study describing the use of CTOs among persons with FEP admitted to two urban EIS in Montreal, Quebec, from 2005 to 2013. At admission, and then annually for 5 years, CTOs data were collected through chart review. Baseline characteristics, assessed by patient interviews, standardized questionnaires and chart review, included socio-demographic data, illness severity, functioning and alcohol and substance use. Descriptive analyses were performed, and FEP patients on CTOs during follow-up and those who were not were compared using analyses of variance, chi-square test and multivariate logistic regression. RESULTS: Among 567 FEP patients, 19.2% were placed on CTOs. The main reasons for requesting CTOs were to prevent further deterioration in mental state, social functioning, harmful behaviours to self and others and homelessness. FEP patients on CTOs had poorer premorbid and baseline functioning, more severe symptoms and social dysfunction at admission, including legal problems and homelessness. CONCLUSIONS: CTOs can be a tool to improve adherence to treatment, which is crucial for relapse prevention in FEP. However, since it is a coercive method that limits a person's fundamental rights, further research is warranted to assess its impact on patients' lives, clinical and functional outcomes, as well as patients' and carers' perception.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".