The effect of resistance training set configuration on strength, power, and hormonal adaptation in female volleyball players
Bibliographic record
Abstract
The primary purpose of this investigation was to determine the impact of altering the set structure during an 8-week resistance training program on anthropometric, hormonal, and strength power characteristics. Thirty female volleyball players were recruited for participation and then randomly assigned to 1 of 3 resistance training groups: (i) cluster sets (CRT; n = 10), (ii) traditional sets (TRT; n = 10), or (iii) control (CON; n = 10). All athletes were evaluated for thigh and arm circumference, vertical jump, 20-m sprint, 4 × 9-m shuttle-run, 1-repetition maximum (1RM) back squat, bench press, military press, and deadlift prior to and after an 8-week periodized training intervention. Blood samples were taken before and after the 8-week training period to evaluate resting testosterone, cortisol, and insulin-like growth factor 1 responses to the training period. After 8 weeks of training the CRT group displayed a small but significant improvement in vertical jump (CRT: effect size (ES) = 038, 7.1%) performance when compared with the TRT group (ES = 0.34, 5.6%). Both the CRT and TRT training interventions resulted in very large increases in the 1RM squat (CRT: 8.4% ± 1.2%; TRT: 7.3% ± 0.6%), bench press (CRT: 8.3% ± 2.0%; TRT: 8.7% ± 1.9%), military press (CRT: 5.7% ± 1.2%; TRT: 5.5% ± 1.6%), and deadlift (CRT: 8.2% ± 1.6%; TRT: 8.3% ± 2.2%). There were no significant differences in 20-m sprint or 4 × 9-m shuttle run times between the CRT, TRT, and CON groups. These results suggest that cluster sets allow for greater improvements in vertical jump performance and equal improvements in strength gains to those seen with traditional sets.
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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) | 0.002 | 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".