Quantification of Training Load in Canadian Football
Bibliographic record
Abstract
The session-rating of perceived exertion (Session-RPE) method for quantifying internal training load (TL) has proven to be a highly valuable and accurate monitoring tool in numerous team sports. However, the influence of frequent impact during Canadian football on the validity of this subjective rating tool remains unclear. The aim of this study was to validate Session-RPE application to a prolonged, intermittent, high-intensity collision-based team sport through correlation of internal TL data collected using 2 criterion heart rate-based measures known as Polar Training-Impulse (TRIMP) and Edwards' TL. Twenty male participants (age = 22.0 ± 1.4 years) from the competitive roster of the University of Saskatchewan Canadian football team were recruited. Session-RPE, Polar TRIMP, and Edwards' TL data were collected daily over the 2011 Canadian Interuniversity Sport pre-competitive and competitive season (11 weeks; 713 total practice sessions). On average, each player contributed 36 sessions of data to the analysis. Statistically significant correlations (p < 0.01) between Session-RPE with Polar TRIMP (r = 0.65-0.91) and with Edwards' TL (r = 0.69-0.91) were found for all individual players. This study provides confirmation that Session-RPE is an inexpensive and simple tool, which is highly practical and accurately measures an individual's response (internal TL) to the Canadian football practice. Furthermore, when considering the number of individuals involved worldwide in collision-based team sports, this tool has the potential to impact a large proportion of the global sporting community.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".