Distinctive Trajectory Groups of Mental Health Functioning among Assertive Community Treatment Clients: An Application of Growth Mixture Modelling Analysis
Bibliographic record
Abstract
OBJECTIVE: Assertive community treatment (ACT) studies that have used conventional, statistical growth modelling methods have not examined different trajectories of outcomes or covariates that could influence different trajectories, even though heterogeneity in outcomes has been established in other research on severe mental illness. The purpose of our study was to examine the general trend in mental health functioning of ACT clients over a 2-year follow-up time period, to discover groups of ACT clients with distinctive longitudinal trajectories of mental health functioning, and to examine if some of the key sociodemographic and illness-related factors influence group membership. METHOD: A 2-year, prospective, within-subjects study of 216 ACT clients within southern Ontario, collected functional outcome data at baseline and 12 and 24 months using the Colorado Client Assessment Record. Baseline covariates included sex, primary diagnosis, number of comorbidities, hospitalization history, and duration of illness. Growth mixture modelling (GMM) was used to examine trajectories. RESULTS: Clinical staff assessments of ACT clients showed a statistically significant improvement in functioning and 84% achieved successful community tenure. GMM analysis identified 2 classes of ACT clients: class 1 (79.63% of clients) experienced lower and stable overall functioning, and class 2 (20.37%) showed a better baseline functioning score and improvement in the overall functioning over time. Class membership was predicted by the number of comorbidities and diagnosis. CONCLUSIONS: Our study suggests general stability in overall functioning for the sampled ACT clients over 2 years, but significant heterogeneity in trajectories of functioning.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".