Investigation of the effect of goal-oriented dance movement therapy on depressive symptoms in people with schizophrenia: a rater-blinded randomised controlled study
Bibliographic record
Abstract
Background/Aims Depression is a common symptom in schizophrenia and can negatively affect treatment. The aim of this study was to examine the effects of goal‑oriented dance or movement therapy on depressive symptoms in individuals diagnosed with schizophrenia. Methods The study was designed as a randomised controlled rater‑blinded study, in which 32 individuals diagnosed with schizophrenia were randomly assigned to either the intervention (dance movement therapy) group or control group. The intervention consisted of twice‑weekly goal‑oriented dance movement therapy sessions, with each session lasting approximately 40–50 minutes, for a total of 8 weeks. Depressive symptoms were evaluated using the Calgary Depression Scale for Schizophrenia. Results The depressive symptoms before and after the intervention were compared. No significant differences between groups were observed at baseline. There was no change in the control group, whereas a significant improvement on depressive symptoms was observed in the total Calgary Depression Scale for Schizophrenia score in the dance movement therapy group after the intervention. Conclusions Goal‑oriented dance movement therapy is an effective treatment for the depressive symptoms in schizophrenia patients in addition to conventional treatments. Although the results are favourable, further studies are needed to test its effectiveness. Implications for practice In the traditional treatment of schizophrenia, it is important to evaluate depression and support the treatment with approaches such as dance movement therapy.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".