Bibliographic record
Abstract
OBJECTIVES: Community treatment orders (CTOs) for people with severe mental illnesses are used across most of Canada. It is unclear if they can reduce health service use, or improve clinical and social outcomes. This review summarizes the evidence from studies conducted in Canada. METHOD: A systematic literature search of PubMed and MEDLINE to March 2015 was conducted. Inclusion criteria were quantitative and qualitative studies undertaken in Canada that presented data on the effect of CTOs on outcomes. RESULTS: Nine papers from 8 studies were included in the review. Four studies compared health service use before and after compulsory treatment as well as engagement with psychosocial supports. Three were qualitative evaluations of patients, family, or staff and the last was a postal survey of psychiatrists. Hospital readmission rates and days spent in hospital were all reduced following CTO placement, while outpatient attendance and participation in psychiatric services and housing all improved. Family members and clinicians were generally positive about the effect of CTOs but patients were ambivalent. However, the strength of the evidence was limited as many of the studies were small, only one included control subjects, and there was no adjustment for potential confounders using either matching or multivariate analyses. Only 2 qualitative studies included the views of patients and their families. CONCLUSIONS: The evidence base for the use of CTOs in Canada is limited and this lack of Canadian research is in marked contrast to other countries where there have been large studies that have used randomized or matched control subjects. Their use should be kept under review.
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 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 | 0.024 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.019 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".