Is Mental Toughness in Elite Athletes a Predictor of Moral Disengagement in Sports?
Bibliographic record
Abstract
The aim of this study was to determine whether mental toughness levels of athletes engaged in the elite-level team and individual sports in Ankara and Erzurum were predictors of moral disengagement in sports. The sample of the study consisted of randomly-selected 515 athletes who were actively engaged in sports in their clubs in 2017. The “Moral Disengagement in Sport Scale-Short” (MDSS) developed by Boardley and Kavussanu (2008) and adapted to Turkish by Gülpınar (2015), and the “Sports Mental Toughness Questionnaire” (SMTQ) developed by Sheard et al. (2009) and adapted to Turkish by Pehlivan (2014) were used as data collection tools. In the analysis of the data, frequency, percentage, Pearson Product-Moment Correlation Coefficient and linear regression analysis were utilized. As a result of the analyzes, no significant difference was found between confidence, one of the sub-scales of the SMTQ, and moral disengagement, but there was a weak positive significant relationship between the constancy and control sub-scales. As a result of the linear regression analysis, it was found that moral disengagement predicted the constancy and control sub-scales significantly and explained 4% and 3% of the variance, respectively, but it was found that the confidence sub-scale was not predicted significantly.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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 teacher head, 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".