Investigating the Influence of Mental Toughness on Risk-Taking Behaviour in Sport.
Bibliographic record
Abstract
Despite the abundance of research and sustained interest in Mental Toughness (MT), very little research has addressed the potentially maladaptive aspects of this construct in sport (e.g., physical toughness, risk-taking, controlling emotions) (Cowden et al., 2020). The purpose of the current study was to investigate the association between MT and self-reported risk-taking behaviours relative to factors that are known to influence risk-taking behaviours in sport. Sport participants with varying sport backgrounds (N =240) completed the Mental Toughness Index (Gucciardi et al., 2015), the Risk, Pain, and Injury Questionnaire (Walk & Wiersma, 2005), the Athletic Identity Measurement Scale (Brewer & Cornelius, 2001) and questions regarding specific risk-taking behaviour (i.e., performing in pain or injury) (Weinberg et al., 2013). Bivariate correlations revealed that MT was unrelated to the RPIQ but significantly and positively related to the AIMS (Social Identity and Exclusivity). Results of a hierarchical regression analysis demonstrated that MT was not a significant predictor of self-reported risk-taking behaviour, whereas the AIMS and the RPIQ Tough subscale were significant predictors. This study builds on previous research that has directly investigated or linked MT and risk-taking in athletes (Bull et al., 2005; Crust & Keegan, 2010; Coulter et al., 2010) and adds insight into the kinds of physical risk-taking behaviours that do not appear to be associated with MT in sport. Furthermore, these results may also point to the stance that mentally tougher athletes can be flexible when it comes to behavioural perseverance in sport (Crust et al., 2016; Gucciardi, 2017).
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".