Authenticity and Mental Toughness in Athletes: An Empirical Model
Bibliographic record
Abstract
The study explores the role of personal authenticity in the psychological training of athletes, focusing on its relationship with intrinsic motivation, mental skills — including stress resilience — and mental toughness. Drawing on data from 355 male athletes (18-26 years) across various sports, standardized tools like the Moscow Authenticity Scale, Mental Strength Scale, Sports Motivation Scale, Ottawa Mental Skills Test, and Connor-Davidson Resilience Scale were employed. Path regression analysis revealed an empirical model that showcases how authenticity linked directly and indirectly to mental toughness through fostering intrinsic motives like self-development, enjoyment of sports, and resilience against stress. While direct contributions of authenticity to mental toughness are modest, its cumulative impact, factoring in mediating effects, is substantial. Notably, authenticity holds more weight for less experienced athletes in developing mental skills and toughness. These findings offer valuable insights for psychologists focused on the psychological training of athletes, especially in managing mental processes crucial for sport performance.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".