Long-Term Athlete Development: Seasonal and Longitudinal Fitness Changes in Female University Rugby Players
Bibliographic record
Abstract
ABSTRACT: Falk Neto, JH, Parent, E, and Kennedy, MD. Long-term athlete development: Seasonal and longitudinal fitness changes in female university rugby players. J Strength Cond Res 35(12): 3459-3465, 2021-The Long-Term Athlete Development model is used by organizations to determine the expected improvements from athletes at different stages of their careers. For female rugby athletes, university sports should provide continued support for athlete development during the Training to Perform stage (18-21 years old). However, little evidence exists for longitudinal changes across years of participation in university sports in this group. The purpose of this study was to assess changes in anthropometric and fitness characteristics in female university rugby players across different seasons. Seventeen players were divided into forwards (n = 9, 21.1 ± 2.6 years) and backs (n = 8, 19.62 ± 0.9 years) and assessed across 5 points over 3 years: preseason 1, postseason 1, preseason 2, postseason 2, and preseason 3. Anthropometric (height, body mass, and body mass index) and performance measurements (grip strength, flexibility, trunk muscular endurance, upper-body muscular endurance, 40-m sprint, vertical jump, and 20-m shuttle run) were collected. V̇o2max was higher at preseason 2 compared with postseason 1 (p = 0.02), postseason 2 (p = 0.01), and preseason 3 (p = 0.015), and trunk muscular endurance was higher at preseason 3 (p = 0.02). Performance in the first 5 m of the sprint test decreased at postseason 1 (p = 0.018) and preseason 2 (p = 0.016) when compared with preseason 1. The results indicate that these female athletes did not improve their fitness across the Training to Perform stage when participating in university sports. These female rugby players likely developed their fitness at earlier stages and, at this stage, already possessed well-developed fitness levels, which were merely maintained throughout years.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".