Warm-Ups and Coaches' Perceptions: Searching for Clues to Improve Injury Prevention in Youth Basketball
Bibliographic record
Abstract
Introduction: Regular use of neuromuscular training (NMT) warm-up programs improves performance and prevents injuries. However, low level of adoption of these programs remains a problem. Understanding the current warm-ups in youth basketball and coaches' perceptions on injury prevention can guide the design of superior implementation strategies. This study describes warm-ups in youth basketball and coaches' injury prevention-related knowledge, attitudes, beliefs, and information sources. Methods: Youth basketball coaches (n = 50) completed a preseason questionnaire. The questionnaire covered warm-up length, use of aerobic/agility/balance/strength/other exercises in the warm-up, injury-related knowledge, attitudes, beliefs, and sources of information. Results: Typical warm-up duration was ≤ 10 min (48.0% of coaches, 95% CI: ±13.8%). All coaches included aerobic exercises in their warm-up. Agility, strength, and balance exercises were utilized by 80.0% (95% CI: ±11.7%), 70.7% (95% CI: ±13.6%), and 26.8% (95% CI: ±13.6%) of coaches, respectively. Most coaches agreed to some extent that basketball injuries are preventable (94%) and that participating in a NMT warm-up program would reduce player's risk of injury (92%). Other coaches were identified as the most common source of information on warm-ups and injury prevention. Discussion: Coaches use parts of effective NMT warm-up programs, but balance exercises are not well adopted. Considering the level of evidence supporting the importance of balance exercises in injury prevention, it is crucial to improve the implementation of NMT warm-up programs in youth basketball, for example, through educational courses. As fellow coaches were identified as the most important source of information, coaches' role in knowledge translation should be emphasized.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".