Effectiveness of an Assertive Community Treatment program for people with severe schizophrenia in mainland China – a 12-month randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Assertive Community Treatment (ACT) is an evidence-based treatment program for people with severe mental illness developed in high-income countries. We report the first randomized controlled trial of ACT in mainland China. METHODS: Sixty outpatients with schizophrenia with severe functional impairments or frequent hospitalizations were randomly assigned to ACT (n = 30) or standard community treatment (n = 30). The severity of symptoms and level of social functioning were assessed at baseline and every 3 months during the 1-year study. The primary outcome was the duration of hospital readmission. Secondary outcomes included a pre-post change in symptom severity, the rates of symptom relapse and gainful employment, social and occupational functioning, and quality of life of family caregivers. RESULTS: Based on a modified intention-to-treat analysis, the outcomes for ACT were significantly better than those of standard community treatment. ACT patients were less likely to be readmitted [3.3% (1/30) v. 25.0% (7/28), Fisher's exact test p = 0.023], had a shorter mean readmission time [2.4 (13.3) v. 30.7 (66.9) days], were less likely to relapse [6.7% (2/30) v. 28.6% (8/28), Fisher's exact test p = 0.038], and had shorter mean time in relapse [3.5 (14.6) v. 34.4 (70.6) days]. The ACT group also had significantly longer times re-employed and greater symptomatic improvement and their caregivers experienced a greater improvement in their quality of life. CONCLUSION: Our results show that culturally adapted ACT is both feasible and effective for individuals with severe schizophrenia in urban China. Replication studies with larger samples and longer duration of follow up are warranted.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".