Effect of Art Therapy on Cognitive and Psychological Well-being of Patients with Major Mental Disorders- An Experimental Study
Bibliographic record
Abstract
Introduction: Psychological and recreational activities have been found to be useful in the field of psychiatry. ‘Art Therapy’ uses therapeutic use of art within the professional role in the personal development of the patient by creating art and finding meaning through them. People can improve their awareness of self and cope with symptoms of stress, anxiety, and traumatic experiences. They can also improve cognitive abilities and gain pleasure in making art. Aim: To evaluate the effectiveness of art therapy on cognitive functions and psychological well-being among mentally ill patients admitted with major mental disorders. Materials and Methods: An evaluative approach with a pre- experimental, one group pre and post-test design was adopted to select 60 patients admitted with major mental disorders in a selected psychiatric unit, Udupi, Karnataka, India through purposive sampling technique. Data collection was done from 1st December 2019 to 31st January 2020. Information was collected through Montreal Cognitive Assessment (MoCA) Scale and Psychological General Well-being Index (PGWBI) Scale. The pretest was conducted on the first day. Art therapy sessions were conducted for four consecutive days. Post-test assessments were done on the seventh and fourteenth day. Results: The mean baseline MoCA score was 16.70±4.04 which improved to 19.10±4.07 on 7th day and 21.28±4.33 on 14th day. Similarly, baseline score of PGWBI was 35.28±13.94 which improved to 53.58±13.88 on 7th day and further increased to 81.80±17.69. Conclusion: Art therapy has a strong effect on the psychological well-being of the patients with mental illnesses and is effective in improving cognitive functions and psychological well-being.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".