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Record W2117306869 · doi:10.1080/17533015.2010.481291

Tipping the iceberg? The state of arts and health in Canada

2010· article· en· W2117306869 on OpenAlexafffundabout
Susan Cox, Darquise Lafrenière, Pamela Brett-MacLean, Kate Collie, Nancy B. Cooley, Janet Dunbrack, Gerri Frager

Bibliographic record

VenueArts & Health · 2010
Typearticle
Languageen
FieldArts and Humanities
TopicArt Therapy and Mental Health
Canadian institutionsDalhousie UniversityUniversity of AlbertaGolder Associates (Canada)Alberta Health ServicesUniversity of British Columbia
FundersCanadian Institutes of Health ResearchHealth CanadaPublic Health Agency of Canada
KeywordsThe artsEnthusiasmPublic relationsSalience (neuroscience)Health promotionHealth careWork (physics)SociologyPolitical scienceSocial sciencePsychologyEngineeringLawSocial psychology

Abstract

fetched live from OpenAlex

The field of arts and health is rapidly gaining momentum in Canada despite the challenges of integration across a vast geography, two official languages and multiple interdisciplinary cultures. Although the field is young, there is a solid foundation of innovative work and great enthusiasm on the part of diverse practitioners about the field's salience and impact. This article provides an overview of the arts and health in Canada and considers work that spans health policy, healthcare practice, individual and community health promotion, health professional education and arts-based health research. A final section offers reflections and recommendations on arts and health in Canada. We provide an online appendix through the journal's website that refers the interested reader to Canadian programs, resources, networks and other materials on the arts and health.

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)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.199
Threshold uncertainty score0.929

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.006
Science and technology studies0.0250.012
Scholarly communication0.0140.004
Open science0.0030.005
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0120.001

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 head0.048
GPT teacher head0.285
Teacher spread0.238 · 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 designNot applicable
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

Citations73
Published2010
Admission routes3
Has abstractyes

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