Assessing Suitability for Short-Term Cognitive-Behavioral Therapy in Psychiatric Outpatients with Psychosis
Bibliographic record
Abstract
OBJECTIVE: The Suitability for Short-Term Cognitive Therapy (SSCT) rating procedure has predicted outcome in depressed and anxious patients. This study examines its relevance in assessing patients with psychosis. METHOD: Outpatients with psychosis (n=56), depression (n=93), and anxiety (n=264) received cognitive- behavioral therapy in a university hospital teaching unit (mean number of sessions=16, SD=11). Demographic, clinical, and suitability variables were assessed as potential predictors of dropout and success as measured by the Reliable Change Index. RESULTS: Despite lower suitability scores in the psychosis group, dropout and success rates were similar across groups, although the magnitude of symptom reduction was less in the psychosis group. Across diagnoses, dropout was predicted by unemployment and by reluctance to take personal responsibility for change. In the psychosis group only, dropout was predicted by hostility. Success of completed therapy was predicted by higher baseline agoraphobic anxiety and "responsibility for change" scores. CONCLUSION: Attention to hostility early in therapy may reduce dropout in psychotic patients. Fostering acceptance of responsibility for change may improve both treatment retention and success across diagnoses. Agoraphobic fear is associated with success, possibly reflecting the effectiveness of behavioral interventions in psychosis and anxiety alike.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".