Mental toughness in sport: The Goal-Expectancy-Self-Control (GES) model
Bibliographic record
Abstract
Mental toughness (MT) has gained considerable attention in sport as an important factor for achieving goals in the presence of varying degrees of pressure, adversity or obstacles. Despite growing interest in MT, it seems that no clear consensus has been reached regarding its conceptualization. In order to broaden the current knowledge on MT, this critical review aims to: (a) critically address MT conceptual issues, (b) identify the most central aspects of MT, and (c) develop a conceptual model to study MT. Following a literature search across four databases, we have critically reviewed the scientific research on the subject. Based on this critical review, we have developed the Goal-Expectancy-Self-Control (GES) model. The GES model posits that when a stressor occurs, three psychological resources characterize MT, namely self-control, self-efficacy, and goals. The GES model captures key components of MT and explains how MT influences athletes’ performance. This model provides a foundation for further research on MT and leads to practical implications.Lay Summary: Mental toughness (MT) is widely used to describe athletes that perform in pressurized circumstances. While it may seem clear what MT means, no consensus has been reached regarding its conceptualization. To guide future research and interventions, we critically reviewed the literature and developed a new conceptual model that focuses on the most central aspects of MT.IMPLICATIONS FOR PRACTICEThe proposed model can lead to the development of effective and specific interventions to enhance mental toughness which target athletes, coaches and sport psychology consultants.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".