Oboe educators’ perspectives on playing-related injury, Part I: Lived experience and perceptions surrounding injury
Bibliographic record
Abstract
Music students experiencing the potentially debilitating effects of playing-related injury (PRI) often first turn to their music teachers for help. This paper aims to document music instructors’ lived experience and perceptions surrounding PRI and better understand how teachers currently support students’ musculoskeletal health. Using a qualitative description approach, in-depth interviews with 10 oboe teachers (7 male, 3 female) documented their lived experience with or without injury and perceptions of PRI. Self-identified uninjured participants ( n = 5) described PRI-adjacent and non-PRI problems which elicited empathy for injured musicians, and self-reflective practices that contributed to better health. Injured participants described varied relationships to their pain, including pain as a source of guilt, distress, learning, and growth, and described diverse coping mechanisms including physical therapy, medication, mindfulness, and self-experimentation. Participants’ observations and experiences of PRI influenced their teaching, and several described seeking greater efficiency in students’ instrument set-up and body use. Resources for injured musicians were perceived to be difficult to access due to financial constraints, unawareness, jargon-filled language, and misinformation. These results suggest a need for more outreach from performing arts health professionals connecting music teachers, often the first point of contact for injured students, with high-quality resources which support student wellbeing.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".