Internal Load of Female Varsity Ice Hockey Players During Training and Games During a Season
Bibliographic record
Abstract
This study quantified internal load, using sessional rating of perceived exertion (sRPE) and heart-rate derived training impulse (TRIMP), of female varsity ice hockey players throughout a season. Twenty-four female (19.8±1.4 yr, 68.0±6.9 kg) varsity ice hockey players participated in this prospective cohort study. Internal load was captured using sRPE and TRIMP for each on-ice session. Internal load was significantly higher (p<0.05) for games (sRPE: 324±202 AU, TRIMP: 95±60 AU) compared to training (sRPE: 248±120 AU, TRIMP: 68±32 AU). Overall, goalies had a higher internal load than forwards (sRPE and TRIMP) and defence (TRIMP), with no differences between forwards and defence. Micro-cycle periodization was present, with training sessions several days prior to game days having the highest internal load (sRPE and TRIMP) and tapering down as subsequent training sessions approached game day. For the meso-cycle assessment, for both training and competition combined, the post-season sRPE was greater than the pre-season (p=0.002) and regular season (p<0.001). Lastly, the association between sRPE and TRIMP, revealed a large, statistically significant relationship (r=0.592, p<0.001). Internal load was greater during competitions, training sessions and subsequent internal loads suggested prioritization around game days, the post-season phase demanded the highest internal load and there was a strong correlation between sRPE and TRIMP.
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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 | 0.001 |
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".