Exploring the relationship between elite athletes' self-regulation ability of the stress response and world ranking using psychophysiological stress response profiling
Bibliographic record
Abstract
The elite sport environment can be very stressful for many athletes. Some athletes cope easily with the multitude of possible stressors associated with the quest for elite athletic performance, many other talented athletes struggle and often fail to achieve their expected performance potential. This research was designed to explore the relationship between elite athletes' self-regulation ability of the stress response and their ranking at the world level using psychophysiological stress assessment profiling. Fifteen elite level athletes' psychophysiological stress response patterns were recorded during a nine stage stress assessment. Respiration rate, heart rate, skin conductance, electromyography, and peripheral body temperature were monitored. There was a significant negative correlation between elite athletes' overall self-regulation ability and their ranking at the world level. Only the EMG-trapezius self-regulation score significantly contributed to the model predicting athletes' world ranking. Our results suggest the existence of a relationship between elite athletes' overall self-regulation ability and their ranking at the world level. This may indicate that higher ranking athletes are better psychophysiological self-regulators. Thus, training for optimal self-regulation of the stress response may enhance sport performance. Acknowledgments: Funding provided by Own the Podium
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".