The Influence of Sport Representation and Attitudes Toward Strength Training on Neuromuscular Performance Profiles in University Athletes: Part II Male Athletes
Bibliographic record
Abstract
ABSTRACT: McClean, ZJ, da Silva Torres, R, Herzog, W, Pasanen, K, Lun, V, and Jordan, MJ. The influence of sport representation and attitudes toward strength training on neuromuscular performance profiles in university athletes: Part I-Male athletes. J Strength Cond Res 40(3): 324-336, 2026-Strength, power, and plyometric testing are essential to evaluate neuromuscular performance in athletes. However, this approach creates datasets with numerous outcome measures that can lead to challenges for interpretation and establishing relevant performance benchmarks for preseason testing and performance-readiness after injury. The idea that athlete performance profiles exist within a larger population has been suggested, but limited research has explored this concept or suggested methodologies for delineating relevant profiles. Exploring the existence of neuromuscular performance profiles in university athletes while accounting for the influence of the sport environment and psychosocial factors, such as attitudes toward strength training, may support more athlete-specific neuromuscular benchmarks. Healthy male university athletes ( n = 272) from 5 sports completed a comprehensive neuromuscular performance testing battery and a questionnaire that included assessment of attitudes toward strength training. Unsupervised machine learning applied to the body weight-normalized neuromuscular performance dataset, along with Fisher's exact tests, was used to examine differences in attitudes toward strength training across clusters (alpha = 0.05). Five profiles were identified, including a high strength/high power/braking-dominant jump strategy cluster with a large ice hockey representation and a high strength/high power/fast jump strategy cluster consisting mostly of field-sport athletes. Differences in attitudes toward training were noted across profiles ( p < 0.05); for instance, athletes in a low-strength/low-power profile tended to prefer training in a more private training environment ( p = 0.023). These results may help inform neuromuscular performance benchmarks in male university athletes, while the psychosocial characteristics of these profiles may provide insight into increasing strength training engagement in this population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".