Mental skills comparison between elite sprint and endurance track and field runners according to their genetic polymorphism: a pilot study
Bibliographic record
Abstract
BACKGROUND: Achieving excellence in track and field athletes requires specific mental skills. The aim of the present study was to compare the mental skills between elite sprint and endurance athletes. METHODS: Forty elite athletes (age 20.55±2.22 years, body mass 74.8±7.9 kg, height 1.70±0.1 m) participated in the present study. The athletes were classified into two groups according to their genetic polymorphism to physical activity: Endurance group (allele I, N.=20) and power group (allele D, N.=20). The mental skills were assessed by means of Ottawa Mental Skill Assessment Tool-3 inventory (OMSAT-3: based in foundation mental skills, psychosomatic skills, and cognitive skills subscales) before the competition period. Furthermore, genetic data were also collected. Sprint and endurance runners were participating in Tunisian National championship. RESULTS: The results showed a significant difference between elite sprint and endurance runners in the foundation mental and psychosomatic skills subscales (all, P<0.05). Typically, the present study revealed that goal setting, commitment, stress reactions, fear control, imagery, competition planning and mental practice were significantly higher among the elite sprint runners compared to the endurance runners (all, P<0.05). Findings from this study could confirm the widely acclaimed research assumption that mental skills, such as goal setting, commitment and mental practice, are the predictor variables of power performances, while endurance performances are associated with different mental skills components. CONCLUSIONS: Finally, the results may inform applied practitioners regarding the differences in mental skill demands between power and endurance athletes and the genetic predisposition of practitioners.
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 | 0.000 | 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 | 0.000 |
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".