Cognitive behavioural group treatment for social anxiety in schizophrenia
Bibliographic record
Abstract
Anxiety symptoms reported by individuals with schizophrenia have been traditionally seen as symptoms associated with the principal disorder and therefore not requiring special attention. The primary aim of this paper is to therapeutically target social anxiety symptoms in individuals with schizophrenia in order to determine the effectiveness of the cognitive behavioural group treatment model as an intervention for social anxiety in this participant group. Thirty-three individuals with schizophrenia and co-morbid social anxiety were allocated to a group-based cognitive behaviour (CBGT) intervention or waitlist control (WLC). Baseline, completion and follow-up ratings consist of measures of social anxiety: the Brief Social Phobia Scale (BSPS), Brief Fear of Negative Evaluation scale (BFNE) and the Social Interaction Anxiety Scale (SIAS); measures of general psychopathology: the Calgary Depression Scale for Schizophrenia (CDSS) and Global Severity Index (GSI) from the Brief Symptom Inventory (BSI); and the Quality of Life, Enjoyment and Satisfaction Questionnaire (QLESQ). Pre- and post-treatment measures were subjected to statistical evaluation. All outcome measures displayed statistical improvement in the intervention group compared with no change in the control group. These treatment gains were maintained at follow-up. CBGT for social anxiety in schizophrenia was demonstrated to be effective as an adjunctive treatment for this population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".