Impact of tapering and proactive recovery on young elite rugbyunion players’ repeated high intensity effort ability
Bibliographic record
Abstract
To assess the effects of a taper combined with proactive recovery on the repeated high intensity effort (RHIE) of elite rugby union players, and the possible interaction of pre-taper fatigue and sleep. Eighteen players performed a 3-week intensive training block followed by a 7-day exponential taper combined with a multicomponent recovery strategy. Following the intervention, players were divided into 3 groups (Normal Training: NT, Acute Fatigue: AF or Functional Overreaching: F-OR) based on their readiness to perform prior to the taper. Total sprint time [TST], percentage decrement [%D] and the number of sprints ≥90% of the best [N90] were analyzed to assess performance during a RHIE test. Subjective sleep quality was assessed through the Pittsburg Sleep Quality Index (PSQI) and the Epworth Sleepiness Scale (ESS). No improvement in TST was reported in either NT or F-OR after the taper, whereas AF tended to improve (-1.58 ± 1.95%; p > 0.05; g = -0.20). F-OR players reported baseline PSQI and ESS indicative of sleep disturbance (6.2 ± 2.2 and 10.6 ± 5.4, respectively). AF displayed a small impairment in PSQI during intensive training (11.5 ± 80.6%; p > 0.05; g = 0.20), which was reversed following the taper (-34.6 ± 62.1%; p > 0.05; g = -0.73). Pre-taper fatigue precluded the expected performance benefits of the combined taper and recovery intervention, likely associated with a lack of strictly controlled intensive training block. Poor sleep quality before the intensive training period appeared to predispose the players to developing functional overreaching.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".