Mental toughness in sport: testing the goal-expectancy-self-control (GES) model among runners and cyclists using cross-sectional and experimental designs
Bibliographic record
Abstract
The Goal-Expectancy-Self-Control (GES) model provides a novel framework to study mental toughness in sport. This model proposes that mental toughness is a state-like multidimensional concept comprising three resources – challenging goals, self-efficacy, and self-control – that operate when athletes encounter a stressor that puts their goal achievement at risk. These resources are proposed to lead to optimal performance through four psychological mechanisms. These include attention, effort, perseverance, and strategies. The purpose of this research was to test this model in endurance sports within the confines of two studies (cross-sectional and experimental). Our samples consisted of 649 runners (Study 1) and 74 trained cyclists (Study 2). Overall, results support the GES model. Taken together, results indicate that mental toughness resources are positively related to endurance performance through the four psychological mechanisms. These findings contribute to a better understanding of mental toughness, as well as underline the importance for athletes to learn how to set challenging goals, attain and sustain high self-control and self-efficacy levels to optimally deploy their psychological mechanisms and reach their goals. Applied implications for athletes, coaches, and mental performance consultants are discussed.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".