Strength and Power Changes During an In-season Resistance Training Program for Male CIS Volleyball Players
Bibliographic record
Abstract
Previous research has indicated that performing repetitions to failure at greater than 80% one repetition max (1 RM) one day per week using a non-‐linear training program can maintain strength and power in many athletic populations. However, this finding has not been directly assessed in male university volleyball players. The purpose of this study was to determine if non-‐linear training two days a week could maintain upper and lower body strength, and vertical jump power and height over the course of a competitive volleyball season, measured at three time points. Eleven trained Canadian Interuniversity Sport (CIS) male athletes were a part of a single training group which completed a maintenance program consisting of non-‐linear training for 17 weeks. At each testing time point, the participants completed a bench press, squat, squat jump, countermovement jump, and three step jump and reach. We predicted that non-‐linear resistance training of mixed intensities twice a week would allow players to maintain strength, vertical jump power, and vertical jump height. Results indicated that upper body strength can be maintained over the course of a competition period with non-‐linear resistance training (p = 0.043). Scores for lower body strength, vertical jump squat jump power, vertical jump countermovement jump power, and vertical jump height increased over the course of the season (p's < 0.001, 0.14, 0.181, and 0.006, respectively). Our results indicate that male volleyball players can benefit from non-‐linear training to maintain or improve performance when training at > 80% 1 RM to failure once a week during a competition season.
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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 | 0.000 | 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) | 0.001 | 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".