Clinical study of dance art therapy on hospitalized patients with chronic schizophrenia
Bibliographic record
Abstract
BACKGROUND: To explore the effect of dance art on the treatment of hospitalized patients with chronic schizophrenia. METHODS: In a prospective randomized controlled study conducted from June 2019 to June 2020, 120 patients from Shanghai Pudong New Area Mental Health Center were divided into intervention (n = 60) and control (n = 60) groups using a random number table. Control patients received standard drug treatment and nursing care, while the intervention group underwent dance art therapy sessions for 90 minutes twice weekly, in addition to standard care. Treatment outcomes after 6 and 12 weeks were measured using the positive and negative symptom scale (PANSS), Wisconsin Card Sorting Test (WCST), Montreal Cognitive Assessment Scale (MoCA), and body mass index (BMI). RESULTS: This study involved 120 male patients with chronic schizophrenia, aged 30 to 60 years. After 6 and 12 weeks, the intervention group showed a greater reduction in PANSS scores (intervention group: from 49.02 ± 2.53 to 37.02 ± 1.83, control group: from 49.08 ± 2.59 to 44.91 ± 2.35, P < .05). In the WCST, the intervention group exhibited a higher increase in classification completion and correct answers, and a greater decrease in errors (P < .05). MoCA scores improved significantly in the intervention group compared to the control group (P < .05). BMI decreased in both groups, with a more pronounced reduction in the intervention group (intervention group: from 26.47 ± 1.05 kg/m² to 22.87 ± 0.73 kg/m², control group: from 26.50 ± 1.03 kg/m² to 26.22 ± 0.80 kg/m², P < .05). CONCLUSION: Based on routine drug treatment and routine nursing care, dance art has a better clinical effect in treating hospitalized patients with chronic schizophrenia, which can improve cognitive function, alleviate clinical symptoms, and reduce BMI.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".