SOCIAL PRESCRIBING FOR THE ARTS: LESSONS FROM ABROAD FOR THE HEALTH CARE OF OLDER AMERICANS
Bibliographic record
Abstract
Abstract Evidence shows the dramatically positive influence of participation in the arts on the health and well-being of older adults (OA). Social Prescribing for the Arts (also called Arts on Prescription or AoP) — in which a professional in a clinical or community setting refers a client to the arts and supports them in accessing arts programming — constitutes one mechanism by which OA may participate in health-promoting arts activities. AoP has been incorporated in other high-income nations including Canada, the UK, and some EU nations. The UK especially has embedded AoP into its healthcare system; however, its universal health insurance program makes its context substantially different from the United States’. This study examined models of AoP abroad and how they could be adapted to the U.S. context for OA. We conducted 25 semi-structured, in-depth interviews with professionals involved in AoP overseas, including physicians, link workers, arts providers, policy experts, and others, as well as 10 interviews with their U.S. counterparts (N=35). Participants were recruited through referral sampling and we intentionally cultivated diversity of role in AoP schemes during recruitment. Transcripts were coded thematically by three team members. Resulting themes included challenges abroad with funding for arts, uneven training among referrers, strain on referrers and arts providers, difficulty reaching the most disadvantaged, skepticism from various stakeholders, the importance of patient involvement in developing a care plan, and a need for open communication between arts providers and referrers. Recommendations for the U.S. include designing and evaluating AoP programs that address these concerns.
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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 | 0.017 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".