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Record W4403347530 · doi:10.1177/2752535x241290666

Use of Subsidized Health Services by Artists in Canada: An Exploratory Study

2024· article· en· W4403347530 on OpenAlexaffabout
Sesinam de Youngster, Shelly‐Anne Li

Bibliographic record

VenueCommunity Health Equity Research & Policy · 2024
Typearticle
Languageen
FieldMedicine
TopicMusicians’ Health and Performance
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsSubsidyDemographicsAnxietyHealth careExploratory researchMedicineNursingPsychologyPublic relationsPsychiatryPolitical scienceSociologyEconomic growthSocial scienceEconomics

Abstract

fetched live from OpenAlex

IntroductionCreative and performing artists are often confronted with precarious employment and insufficient healthcare coverage. A clinic in Canada that provides specialized healthcare to artists offers eligible artists subsidized health services. We aim to compare the use of health services, demographics and health conditions between subsidy artist recipients (SAs) and non-subsidy artists (NSAs).MethodsWe accessed existing data from 265 SAs and 711 NSAs and applied descriptive and inferential statistics to address our research questions.ResultsMusculoskeletal issues, stress, anxiety disorders, and depressive disorders are the most common health problems faced by SAs. Compared to NSAs, SAs were more likely to seek treatment for stress, but less likely to seek treatment for anxiety disorders, depressive disorders, chronic problems, and upper extremity problems.DiscussionFuture research may investigate the enduring effects of subsidized health services on SAs' health outcomes. Sustained positive outcomes are crucial for maintaining an artist's career and well-being.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.402
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.410
GPT teacher head0.554
Teacher spread0.143 · 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 teacher head, not a consensus.

Study designObservational
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

Citations0
Published2024
Admission routes2
Has abstractyes

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