Canadian Treatment Guidelines on Psychosocial Treatment of Schizophrenia in Adults
Bibliographic record
Abstract
OBJECTIVE: It is generally recognised that psychosocial interventions are essential components of the effective treatment of schizophrenia in adults. A considerable body of research is being published regarding the effectiveness of such interventions. In the current article, we derive recommendations reflecting the current state of evidence for their effectiveness. METHODS: Recommendations were formulated on the basis of a review of relevant guidelines, particularly those formulated by the Scottish Intercollegiate Guideline Network (SIGN) and National Institute for Health and Care Excellence (NICE). RESULTS: There is evidence strongly supporting the use of family interventions, supported employment programs, and cognitive-behavioural therapy. There are also reasons to recommend the use of cognitive remediation, social skills training, and life skills training under specified circumstances. It is important that all patients and families be provided with education about the nature of schizophrenia and its treatment. Several recent innovative psychosocial approaches to treatment are awaiting more thorough evaluation. CONCLUSIONS: There continues to be strong evidence for the effectiveness of several psychosocial interventions in improving outcomes for adults with schizophrenia. In the past decade, innovative interventions have been described, several of which are the subject of ongoing evaluative research.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".