Training for Sustained Performance: Moving Toward Long-Term Musician Development
Bibliographic record
Abstract
Success in the performing arts, like sports, is dependent upon the acquisition and consistent use of a diverse range of skills. In sports, an understanding of safe and effective use of the body is required to facilitate long-term involvement in that activity. In order to assist athletes to attain their performance goals, and ensure healthy and sustained involvement, long-term athlete development (LTAD) models have been devised and adapted by professional sporting bodies throughout the world. LTAD models emphasize the intellectual, emotional, and social development of the athlete, encourage long-term participation in physical activities, and enable participants to improve their overall health and well-being and increase their life-long participation in physical activity. At present there is no such long-term development model for musicians. Yet musicians must cope with a multitude of career-related physical and mental demands, and performance-related injuries and career burnout are rife within the profession. Despite this, musicians' training rarely addresses such issues and musicians are left largely to learn about them through either chance or accrued experience. This paper discusses key concepts and recommendations in LTAD models, together with music-specific research highlighting the need for the development of a comprehensive long-term approach to musicians' training. The results of a survey of existing music training programs are compared to recommendations and the different development stages in LTAD models. Finally, implementation science is introduced as a methodological option for identifying how best to communicate the body of evidence-based knowledge concerning healthy and effective music-making to young student musicians.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".