An exploration of differential ratings of perceived exertion and training load in women's collegiate ice hockey
Bibliographic record
Abstract
This study explored the relationship between differential ratings of perceived exertion (dRPE) and ratings of perceived exertion (RPE), and internal training load in a sample of collegiate ice hockey players. Twenty-six female (20 ± 1.66 yr, 169.1 ± 6.6 cm) varsity ice hockey players participated over seven weeks in the preseason and regular season. RPE, dRPE (i.e., breathlessness, lower limb exertion, and technical/cognitive demand), and practice duration were recorded each day following completion of on-ice training. RPE-breathlessness, RPE-lower limb exertion, and RPE-technical/cognitive explained approximately 75% of the variance seen in RPE. Differential RPE explained the most variance in RPE for goaltenders (R 2 = 0.86) followed by defenders (R 2 = 0.71) and forwards (R 2 = 0.70). RPE-breathlessness showed the strongest relationship with RPE, regardless of playing position. On average, internal training load was greatest on days furthest from match days, and tapered as training day approached match days. Overall, dRPE explained a statistically significant amount of the variance captured by RPE, indicating that it captures relevant information beyond only RPE. Using RPE and dRPE to measure internal training load is a low-cost and feasible method that can be used to assist athlete load monitoring in a female collegiate varsity ice hockey setting.
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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 | 0.001 | 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) | 0.001 | 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".