Effects of art therapy in cancer care: A systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effect of art therapy on cancer patients' quality of life and physical and psychological symptoms. METHODS: The databases PubMed, Embase, Web of Science, The Cochrane Library, Clinical Trial.gov, the China National Knowledge Infrastructure (CNKI), Wanfang and the Chinese Biomedical Literature Database (CBM) were searched from their inception up to 20 August 2019. Trials examining the effects of art therapy on physical and psychological symptoms and quality of life versus a control group were included. The methodological quality of the included randomised controlled trials was assessed using the risk of bias tool of Cochrane Handbook. Meanwhile, the Newcastle-Ottawa Quality Assessment Scale (NOS) was used to evaluate the methodological quality of the non-randomised studies. RESULTS: Twelve studies involving 587 cancer patients were included. The results revealed that art therapy significantly reduced anxiety symptoms (standard mean difference [SMD] = -0.46, 95% confidence interval [CI] [-0.90, 0.02], p = .04), depression symptoms (SMD = -0.47, 95% CI [-0.72, 0.21], p < .01), and fatigue (SMD = -0.38, 95% CI [-0.68, -0.09], p = .01) in cancer patients. Art therapy also significantly improved the quality of life of cancer patients (SMD = 0.43, 95% CI [0.18, 0.68], p < .01). CONCLUSIONS: Art therapy had a positive effect on quality of life and symptoms in cancer patients and can be used as a complementary treatment for cancer patients.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.023 | 0.036 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".