The Effect of the Art Therapy Interventions on Depression Symptoms Among Older Adults: A Meta-analysis of Controlled Clinical Trials
Bibliographic record
Abstract
Background To our knowledge, no systematic reviews and meta-analyses have yet been published that examine the effect of art therapy (AT) interventions on depression symptoms among older adults, and this study aimed to systematically review and meta-analysis of clinical trials, summarize eligible relevant studies and provide a true effect measure for the association between AT and depression symptoms in older adults. Methods The databases of PubMed, Web of Science, Scopus, and the Cochrane Central Register of Controlled Trials were searched until 15 February 2022. The methodological quality of the included studies was evaluated by the Delphi checklist. The heterogeneity across studies was conducted by chi-squared test and measured its quantity by the I 2 statistic. We performed this meta-analysis to obtain a summary measure of the mean difference in depression scores between AT and control groups using a random-effects model. All statistical analyses were carried out at a significance level of .05 using Stata software, version 14. Results Until 15 February 2022, 222 studies through databases and 199 studies through review of references were included in the present meta-analysis. In total, the analysis covered 8 studies. The difference in mean depression score between the intervention and control groups showed significant reductions in the AT group (MD −.78; 95% CI: −1.17, −.38; I 2 = 67.9%). Conclusion Our findings suggest that AT can be considered an effective intervention for reducing depression symptoms among older adults and art therapists/psychotherapists can use this method to reduce the symptoms of depression among older adults.
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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 | 0.036 | 0.074 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.065 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".