Creative Art: Connection to Health and Well-Being
Bibliographic record
Abstract
This paper explores the association between creativity and health. We examined the literature to investigate if participating in creative arts such as dance and movement, music, visual art therapy, and creative writing has a beneficial effect on health and well-being. Recently, there has been an increased interest in studying how participatory and receptive arts can enhance wellness. Increasingly new research shows a correlation between creativity, improved feelings of well-being, and other positive health outcomes. Studies in this area indicate that engaging in creative arts brings about psychosocial, physiological, and behavioural responses that may help to decrease loneliness, depression, pain, and many other health-related issues. We reviewed and analyzed how creative arts can create channels for expressing emotions and improve physical, mental, and spiritual health. In this review, we define and discuss: 1) Creative Art Activities and Health; 2) Receptive and Participatory Engagement: Well-Being and Social Connectedness; 3) Health and Well-being in Later Stages of Life; 4) Art therapy and Children; 5) Creativity and Well-Being during COVID-19; 6) Specific Art Therapies and Their Beneficial Effects. We anticipate this review could underpin further research in health promotion. We also aim to encourage partnerships between the fields of health and creative arts to develop strategies to further their collaboration.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".