Are Self-Talk and Mental Toughness Level Prerequisites Besides the Kick Boxing Education Level in Athletes?
Bibliographic record
Abstract
The aim of this study is to determine the relationship between self-talk and mental toughness levels of kickboxers and to compare the differences between the self-talk and mental toughness of the athletes according to the independent variables obtained from the personal information of the athletes participating in the research. The study group of the research consists of athletes who participated in kickboxing competitions in Turkey (n=223). 120 (53.8%) of the athletes are women and 103 (46.2%) are men. In the study, the “Self-Talk Questionnaire” adapted to the Turkish athlete population by Engür (2011) and the “Sport Mental Toughness Questionnaire” adapted to Turkish by Altıntaş and Bayar Koruç (2016) is used. It is decided whether the data met the prerequisites of the parametric tests by examining the Skewness and Kurtosis values (normal distribution of the data) and the Levene test (equality of variance) results. As a result, correlation analysis is used to determine the relationships between the variables, and t-test and ANOVA analyses are used to determine the differences. As a result of the research, it is determined that there is a positive and significant relationship between self-talk and mental toughness. Mental toughness is accepted as a term that trainers, managers and athletes attach importance to, and it is considered a prerequisite for sportive success. The fact that there is a positive and significant relationship between self-talk and mental toughness suggests that self-talk is also a predictor of performance. At the point of achieving success, the inner messages that the athlete will give themselves will increase their mental toughness and will be reflected in the sports environment, training or competition.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".