The Influence of Sport Representation and Attitudes Toward Strength Training on Neuromuscular Performance Profiles in University Athletes: Part I Female Athletes
Bibliographic record
Abstract
ABSTRACT: McClean, ZJ, da Silva Torres, R, Herzog, W, Pasanen, K, Lun, V, Nimphius, S, and Jordan, MJ. The influence of sport representation and attitudes toward strength training on neuromuscular performance profiles in university athletes: part I female athletes. J Strength Cond Res 40(3): 311-323, 2026-Neuromuscular performance is often assessed across strength, power, and plyometric tasks in athlete populations with an increasing emphasis on biomechanical testing that yields multifaceted data sets with several outcome measures per task. This leads to specific challenges in determining relevant neuromuscular performance benchmarks especially when considering contextual factors related to the sport environment. Thus, characterizing athlete neuromuscular performance subpopulations (or profiles) while accounting for the influence of contextual factors such as those related to the sport environment, and psychosocial factors such as the athlete's attitudes toward strength training could provide a framework for more specific neuromuscular benchmarks. Therefore, neuromuscular performance profiles were explored in female university athletes while accounting for sport influences and attitudes toward strength training. Female university athletes ( n = 192) from 5 sports completed a comprehensive lower body neuromuscular performance testing battery and surveys assessing attitudes toward strength training. Clustering was done to define neuromuscular performance profiles and Fisher's Exact Tests were used to examine differences in attitudes toward strength training across clusters. A high-strength/high-power/high-stiffness cluster, low-strength/low-power/low-stiffness cluster, and low-strength/low-power/high-stiffness cluster were identified with no difference in sport representation but differences in attitudes toward strength training ( p < 0.047; alpha = 0.05). These findings may help to quantify the characteristics of different female university athlete neuromuscular performance profiles while accounting for potential sport and psychosocial influences. One potential application of these results is to inform neuromuscular performance benchmarks that can be used in conjunction with preseason evaluations or in the context of restoring performance readiness after injury.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".