Bibliographic record
Abstract
For any skilled performer to deliver their optimal performance, preparation must extend beyond task-specific skill development to include psychological skills training, physical conditioning, and injury prevention. The keynote lecture upon which this article is based (delivered at the International Symposium on Performance Science 2021) explored current research that demonstrates the importance of physical conditioning and injury prevention for drummers (i.e., percussionists who play the drum kit). Early results revealed that professional drummers' heart rates during live performances can reach similar levels to those of other professional athletes during competitions. They also established that playing-related musculoskeletal disorders (PRMDs) are very common in drummers, particularly those affecting the upper limbs such as tendinitis and carpal tunnel syndrome. Evidence from laboratory-based studies supports non-neutral postures, repetitive movements, and exposure to hand-arm vibration as risk factors for the development of these injuries in drummers. Embedding injury prevention education within drum kit curricula is a promising strategy for reducing the rates at which drummers report experiencing PRMDs, and the barriers and facilitators that drum kit educators encounter when attempting to do so are currently under investigation. When drummers include both physical conditioning and injury prevention within their overall preparation regimen, they will maximize their potential to deliver their peak performance.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".