Community Tenure of People With Serious Mental Illness in Assertive Community Treatment in Canada
Bibliographic record
Abstract
OBJECTIVE: This study followed consumers after admission to an assertive community treatment program to determine when the first hospital admission was more likely to occur, which variables predicted community tenure, and, more specifically, whether the availability of within-program hospital beds predicted community tenure. METHODS: Data were gathered from three assertive community treatment programs in southeastern Ontario--the psychosocial rehabilitation program, the community integration program, and the assertive community treatment team program. Only the psychosocial rehabilitation program provided within-program beds. Hospital records of consumers who entered a program between July 1, 1990, and December 1, 1999, were examined prospectively until January 1, 2000, in order to record time to the first admission. Survival analysis based on the life-tables method was used to estimate the probability of remaining out of the hospital at 90-day intervals. Factors associated with time to admission were identified by using the Cox proportional hazards model. RESULTS: A total of 333 consumers were followed: 117 consumers in the psychosocial rehabilitation program, 105 in the community integration program, and 111 in the assertive community treatment team program. Findings indicated that consumers were most likely to be admitted to a hospital in the nine months after entering an assertive community treatment program. A diagnosis of substance use disorder, higher past hospital use, and the availability of within-program beds were associated with an increased risk of admission. CONCLUSIONS: Studies have shown that hospitalization remains a reality for many consumers and therefore warrants further study. The survival model proved advantageous by allowing a more complete and comparable description of consumers' hospitalization patterns that cannot be achieved with previously used methods, and it offered the power of regression analysis.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".