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Record W2724353214 · doi:10.1097/jom.0000000000001052

Occupational Health and the Performing Arts

2017· review· en· W2724353214 on OpenAlexaff
David Hinkamp, Jennie Morton, Donna Krasnow, M. Virginia Wilmerding, William J Dawson, Michael G. Stewart, H. Steven Sims, Jan Potter Reed, Katherine Duvall, Michael McCann

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2017
Typereview
Languageen
FieldMedicine
TopicMusicians’ Health and Performance
Canadian institutionsDawson College
Fundersnot available
KeywordsThe artsPerforming artsDanceOccupational safety and healthMedicineMartial artsPerforming arts educationArts administrationPublic relationsMedical educationPsychologyArts in educationPolitical scienceVisual artsPathologyArt

Abstract

fetched live from OpenAlex

OBJECTIVE: Workplace hazards in the performing arts cause injuries, disabilities, and deaths every year. Occupational health professionals are familiar with most of these hazards and are particularly qualified to contribute to efforts to reduce them. This article reviews current health issues in the performing arts and highlights opportunities for occupational health contributions. METHODS: Recognized experts in performing arts medicine were consulted and articles illustrating performing arts health issues were reviewed. Literature sources included medical databases, unindexed art-health publications, and popular press articles. RESULTS: Resources discussing hazards and health issues in theater, dance, voice, and instrumental musicians were located and reviewed. CONCLUSIONS: Treatment providers have a history of involvement with segments of the performing arts. The occupational health approach to workplace health issues can effectively complement these efforts. Sources of further information on performing arts health concerns are available.

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)0.001
metaresearch head score (Gemma)0.001
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: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0090.002

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 head0.198
GPT teacher head0.444
Teacher spread0.246 · 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
GenreReview

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

Citations22
Published2017
Admission routes1
Has abstractyes

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