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Record W4375932700 · doi:10.1515/psych-2022-0133

Toward a New Science of the Clinical Uses of the Arts

2023· article· en· W4375932700 on OpenAlexafffund
Steven Brown, Jacob Cameirao

Bibliographic record

VenueOpen Psychology · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicArt Therapy and Mental Health
Canadian institutionsMcMaster University
FundersUniversity of HaifaSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaUniversiteit van AmsterdamEmory University
KeywordsThe artsVariety (cybernetics)Art therapyPsychologyComputer scienceEpistemologyPsychotherapistArtVisual artsPhilosophyArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract The arts are used clinically in a wide variety of applications, spanning from physical therapy to psychotherapy. We present a theoretical analysis of these clinical applications that is grounded in a unified model of the arts. Such an approach is based on an understanding of the relationships among the various art forms and how the arts are able to impact non-art functions via transfer effects. A unified model helps to clarify the distinction between near and far transfer in the clinical uses of the arts. The empirical evidence suggests that art applications for physical therapy tend to be based on near-transfer effects and show high specificity for the employed art forms. By contrast, art applications for psychotherapy tend to be based on far transfer and show less specificity for the employed art forms. We argue that a theory of the clinical uses of the arts has to be predicated on a unified model of the arts themselves. Such a model provides a rational basis for understanding how art forms are able to bring about their clinical effects.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.002
Science and technology studies0.0030.084
Scholarly communication0.0120.020
Open science0.0020.006
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.484
GPT teacher head0.524
Teacher spread0.039 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2023
Admission routes2
Has abstractyes

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