Investigation of Mental Toughness Levels of Individuals Who Actively Do Sports: A Sample of the City of Elazig
Bibliographic record
Abstract
The aim of this study is to investigate the mental toughness levels of individuals who actively do sports in the face of events they face during sportive performances. Mental toughness is among the psychological characteristics to achieve the highest performance by athletes and coaches. Mental toughness is also important in terms of evaluating the performances of athletes and support their development. The population of the study consisted of individuals who actively do sports in the city of Elazig while the sample of the study consisted of 156 active athletes, who were chosen by the simple random sampling method. As the data collection tools, the personal information form, which was created by the researchers, and the Mental Toughness Scale (MTS), which was developed by Madrigal et al. (2013) and adapted into Turkish by Nevzat Erdogan by 2016, were used. In conclusion, of the athletes who participated in the study, it was observed that male athletes had higher levels of mental toughness compared to female athletes according to the gender variable. Furthermore, no significant differences were observed in terms of the variables of marital status, age, educational status, sports experience and sports branch.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".