Bibliographic record
Abstract
INTRODUCTION: Sports performance optimization relies heavily on the balance between increasing training load (TL) and appropriate recovery. In high performance settings, the crucial role that athlete monitoring plays in this intricate balancing act is widely recognized. PURPOSE: Due to the violent and unique demands of Canadian football, minimal research and few practical monitoring tools are available for coaches and practitioners. The thesis aim therefore, is to provide a body of research that begins to address athlete monitoring challenges in Canadian football. CHAPTER III: Study 1 was designed to validate the Session-Ratings of Perceived Exertion (sRPE) method of quantifying internal TL in football players. Statistically significant correlations for all individual players between sRPE and two criterion heart rate-based measures were found. Results confirm that sRPE is a highly practical and valid tool for Canadian football application. CHAPTER IV: Despite frequent use in other sports, the high injury occurrence in football often prevents consistent neuromuscular fatigue (NMF) monitoring using a maximal countermovement jump (CMJ). Further, little direct evidence exists supporting the relationship between athlete CMJ performance and NMF. Study 2 addressed these issues by assessing the acute-fatiguing effects of a game simulation (G-Sim) on postural sway (PS), CMJ performance and lab-based NMF measures in football players. Congruency between all measures post G-Sim suggests that submaximal PS monitoring has the potential to supplement CMJ in NMF tracking of football players hampered by minor injuries. CHAPTER V: Recognizing that acute-fatiguing effects may misrepresent fatigue across extended training periods, study 3 applied previous methodology (study 1 & 2) to evaluate PS as a valid NMF indicator over a competitive 11-week season. Significant associations between both CMJ and PS performance with weekly Global TL fluctuations provided evidence of NMF assessments valid across a football season. There was no evidence of differences in NMF status between starters and non-starters of the weekly game. CONCLUSION: Thesis findings confirm the validity and practicality of sRPE and the NMF monitoring tools of CMJ and PS across a competitive football season. This initial work provides a spring-board for future research as it has broadened our knowledge of athlete responses to- and monitoring in- Canadian football.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".