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Record W1806570625 · doi:10.3233/wor-2011-1230

The lived experience of working as a musician with an injury

2011· article· en· W1806570625 on OpenAlexaffabout
Christine Guptill

Bibliographic record

VenueWork · 2011
Typearticle
Languageen
FieldMedicine
TopicMusicians’ Health and Performance
Canadian institutionsMcMaster UniversitySt. Joseph’s Healthcare HamiltonHand and Upper Limb ClinicWestern University
Fundersnot available
KeywordsLived experienceHealth careQualitative researchHealth professionalsPsychologyFocus groupPhenomenology (philosophy)NursingPhenomenological methodMedical educationMedicinePsychotherapistSociologySocial science

Abstract

fetched live from OpenAlex

OBJECTIVE: Research and clinical experience have shown that musicians are at risk of acquiring playing-related injuries. This paper explores findings from a qualitative research study examining the lived experience of professional instrumental musicians with playing-related injuries, which has thus far been missing from the performing arts health literature. METHODOLOGY: This study employed a phenomenological methodology influenced by van Manen to examine the lived experiences of professional musicians with playing-related injuries. PARTICIPANTS AND METHODS: Ten professional musicians in Ontario, Canada were interviewed about their experiences as musicians with playing-related injuries. Six of the participants later attended a focus group where preliminary findings were presented. RESULTS: The findings demonstrate a need for education about risk and prevention of injuries that could be satisfied by healthcare professionals and music educators. CONCLUSIONS: The practice and training of healthcare professionals should include the "tactful" (van Manen) delivery of care for this important and vulnerable population.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.017
Scholarly communication0.0030.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.093
GPT teacher head0.316
Teacher spread0.223 · 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 designQualitative
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

Citations37
Published2011
Admission routes2
Has abstractyes

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