If Art Were a Drug: Implications for Parkinson’s Disease
Bibliographic record
Abstract
Parkinson's disease (PD) is a chronic and complex neurodegenerative disorder. Conventional pharmacological or surgical therapies alone are often insufficient at adequately alleviating disability. Moreover, there is an increasing shift toward person-centered care, emphasizing the concept of "living well". In this context, arts-based interventions offer great promise, functioning as platforms for creative expression that could provide novel mechanisms to promote quality of life. Here we present a qualitative review of arts-based interventions for PD, including music, dance, drama, visual arts, and creative writing. For each, we discuss their applications to PD, proposed mechanisms, evidence from prior studies, and upcoming research. We also provide examples of community-based projects. Studies to date have had relatively small sample sizes, but their findings suggest that arts-based interventions have the potential to reduce motor and non-motor symptoms. They may also empower people with PD and thereby address issues of self-esteem, foster personal problem-solving, and augment holistic well-being. However, there is a paucity of research determining optimal dosage and symptom-specific benefits of these therapies. If art were a drug, we would have to perform appropriately powered studies to provide these data before incorporating it into routine patient care. We therefore call for further research with properly designed studies to offer more rigorous and evidence-based support for what we intuitively think is a highly promising approach to support individuals living with PD. Given the possible positive impact on people's lives, arts-based approaches merit further development and, if proven to be effective, systematic inclusion within integrated management plans.
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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 | 0.010 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".