Art therapy among palliative cancer patients: Aesthetic dimensions and impacts on symptoms
Bibliographic record
Abstract
OBJECTIVE: This study aimed to explore whether aesthetic beauty and the pleasure that results from artistic activity can contribute to a reduction in the symptoms experienced by palliative care patients, and to improve the effectiveness of art therapy sessions. METHOD: A self-assessment of six symptoms (pain, anxiety, ill-being, tiredness, sadness, and depression) adapted from the Edmonton Symptom Assessment System (ESAS) was completed by patients before and after a one-hour art therapy session. This assessment was completed after the session with a self-assessment of aesthetic feeling. A correlation analysis was then performed. RESULTS: From July of 2012 to December of 2013, 28 patients took part in 63 art therapy sessions. On the whole, these sessions reduced the global distress of patients by 47% (p < 0.0001). There was a significant reduction in all the symptoms studied; pain (p = 0.003), anxiety (p < 0.0001), ill-being (p < 0.0001), tiredness (p < 0.0001), sadness (p < 0.0001), and depression (p < 0.0001). A study of the significant correlations (0.35 < rs < 0.52, p < 0.05) indicated that technical satisfaction, aesthetic beauty, and pleasure are all involved to varying degrees in reduction of symptoms. SIGNIFICANCE OF RESULTS: Our findings confirm the benefits of art therapy in reducing distress within the palliative context. We also make suggestions for the future direction and improvement of these sessions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".