Toward a Unification of Arts Applications for Health
Bibliographic record
Abstract
Arts applications for health – whether for clinical treatment or its complement in health promotion – tend to be “canalized”: research and practice in one art domain tend to be done in isolation from research and practice in other art domains. As a result, arts applications for health lack a unifying theoretical framework that would allow for the rational selection and inclusion of the arts into programs of health. In this thesis, I present two theoretical articles that address this problem. The first article, Toward a New Science of the Clinical Uses of the Arts, proposes a framework unifying clinical arts applications. It argues for the equivalence of psychotherapies (whether arts-based or not) in treating mental illness because all psychotherapies rely on the same set of common therapeutic factors in producing their clinical effects. In contrast, we argue for the non-equivalence (i.e., the specificity) of physical therapies (whether arts-based or not) in treating physical illness since physical therapies rely on specific therapeutic factors unique to each therapy in producing most of their clinical effects. The second article, Toward a Unification of Arts Applications for Health Promotion, proposes a framework for unifying the arts as leisure activities for health promotion. I propose that all leisure activities (whether arts-based or not) rely on a set of five common health-promoting factors in producing their effects. This results in an equivalence of outcomes when any two leisure activities possess the same health-promoting factors. Arts applications for both clinical treatment and health promotion show similarities in that both operate via “transfer effects,” whereby the arts transfer benefits to non-arts health domains. The arts tend to improve mental health via “far” transfer effects, whereas they tend to improve physical health via “near” transfer effects.
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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 | 0.034 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.006 | 0.062 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 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".