The Effects of Group Arts Therapy Based on Emotion Management Training on the Emotional Expression, Alexithymia, Depression and Quality of Life in Patients with Schizophrenia
Bibliographic record
Abstract
Aims: The objective of this study was to investigate the effects of group arts therapy based on emotion management training on emotional expression, positive and negative emotion, alexithymia, depression and quality of life patients with schizophrenia. Methods: 24 patients of 160 inpatients with schizophrenia from H Mental Health Care Facilities in G city were randomly assigned to either an experimental or control group. Each group were consist of 12 patients. Group arts therapy was conducted on the experimental group twice a week, 60 minutes per session, for a total of 16 sessions. The following scales were used for assessment: Berkeley Expressivity Questionnaire (BEQ), Positive Affect and Negative Affect Schedule (PANAS), Toronto Alexithymia Scale (TAS-20K), Depression Scale for Schizophrenia (K-CDSS), and Schizophrenia Quality of Life Scale (SQLS). Repeated measures ANOVA was conducted to confirm the differences for scores of each scales regarding groups, measuring timing, and also the interaction between groups and measuring timing. Results: Group arts therapy increased emotional expression (total score, expressivity and impulse strength subscale socre of BEQ) in experimental group compared with control group (p<.001). Group arts therapy increased positive emotion (p<0.001) as well as decreased negative emotion (p<0.05) in experimental group compared with the control group. Group arts therapy increased score of difficulty identifying feelings of TAS-20 (p<0.01) in experimental group compared with the control group. Group arts therapy decreased depression (p<0.01) in experimental group compared with the control group. Group arts therapy increased quality of life (p<0.01) in experimental group compared with the control group. Conclusion: The group arts therapy significantly improved the emotional expression and positive emotion, and decreased negative emotion, alexithymia and depression in patients with schizophrenia and also improved quality of life. These results suggest that group arts therapy based on emotion management training could be a useful intervention for emotional disturbance in patients with schizophrenia.
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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 | 0.000 |
| 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".