Community Treatment Orders in Toronto: The Emerging Data
Bibliographic record
Abstract
OBJECTIVE: Over a 4-year period in Toronto, this study aimed to compare individuals on a community treatment order (CTO) with individuals not on a CTO in terms of sociodemographic and clinical variables, hospital use, and continued engagement with health services on exit from the case management program. Hospital stay reductions from preadmission into the program to various postadmission periods were compared across the 2 groups. METHODS: Descriptive statistics and tests of statistical significance (chi-square and t test) were run on regularly collected administrative data for both groups. RESULTS: Categorical data analysis indicated the 2 groups were statistically similar on a range of sociodemographic and clinical variables. Although both groups displayed reductions in hospital use, the CTO group displayed a significantly higher reduction in cumulative days in hospital per hospital admission within both the first and second 6-month period postadmission. This same group also had significantly greater reduction in hospital admissions during the second 6-month period postadmission. The CTO group also had a significantly higher portion of individuals exiting the program within these first two 6-month periods; as well, they were less likely to exit with support such as case management or assertive community treatment and more likely to continue with ongoing medical supervision than the comparison group. CONCLUSION: Although we were unable to rule out regression to the mean for hospitalization reductions, the Toronto experience has shown that CTOs are helpful in assisting individuals who historically refused services to remain engaged with treatment and support services. The study also calls for broadening operational measures of outcomes for CTO studies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".