Pain-related and performance anxiety and their contribution to pain in music students: a pilot study
Bibliographic record
Abstract
Background Pain complaints are common among musicians, whose occupation is highly demanding on both a physical and a psychological level. The purpose of the present study was to better understand the severity of musculoskeletal pain in orchestra musicians by measuring the potential contributions of biological (medical diagnosis), psychosocial (age, gender, instrument, practice and exercising history, and occupational satisfaction), and psychological (pain-related anxiety, performance anxiety, and affect) variables. Participants and procedure Data were collected from 59 music students playing in a symphonic orchestra. Univariate analyses were performed to assess differences in biological, psychosocial, and psychological predictors, using the presence or absence of pain as the dependent variable. Regression analyses were performed to develop a model of variance to explain the severity of pain. Results The results revealed lower occupational satisfaction to be associated with the presence of pain. However, a greater proportion of variance (31%) in pain severity was explained by pain-related anxiety combined with performance anxiety. Thus, the model that would best explain playing-related pain in musicians would need to focus mainly on psychological variables, namely pain-related and performance anxiety. Conclusions Further investigation is needed to determine how treatment of musculoskeletal pain in musicians should address these psychological variables.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".