The Lived Experience of Orchestral String Musicians with Playing Related Pain
Bibliographic record
Abstract
OBJECTIVE: Rates of pain are high among musicians, and string musicians may be particularly at risk. The aim of the study was to investigate the lived experience of orchestral string musicians with playing-related pain. METHODS: The study used a Heideggerian phenomenological approach. Five professional and university-level string musicians were interviewed about their experience of playing-related pain, and transcriptions of their interviews were analysed using thematic analysis. RESULTS: Participants engaged in a variety of types of musical performance, however they described orchestral playing as contributing the most to their pain. Pain led to increased focus on the body and less engagement in the music. They experienced a sense of loss in multiple domains of their lives, yet also described personal growth as a result of their pain. Participants were more likely to disclose their pain in student orchestras than in professional ones. CONCLUSION: Pain impacts multiple domains of musician's lives, and therefore must be addressed holistically by healthcare providers. While musicians are finding that it is becoming more acceptable to discuss their pain, pain is still not adequately addressed. Understanding the experience of musicians with playing-related pain could help healthcare professionals to better serve this unique population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".