Bibliographic record
Abstract
In order to achieve the expected high performance, athletes must be physically, technically, tactically and socially ready as well as being psychologically ready and strong (Erdoğan & Kocaekşi, 2015). In this context, mental training of athletes is also important. Mental training means that athletes adjust and control their own sports behavior by adopting specific ways to promote psychological state. Aim of this research was to determine the level of mental training application of professional athletes and differences according to some variables. The sample consisted of 485 professional athletes (University Students) who are still competing in 4 different sports in Turkey (football, handball, basketball and volleyball). Data collection tool consisting of two parts was used in the research. In the first part of the data collection tool, a questionnaire consisting of the personal information of the participants was used. In the second part, Developed by Benkhe et al. (2017) and adapted to Turkish by Yarayan and İlhan (2018), “Mental Training in Sports Inventory” consisting of 5 sub-dimensions and a total of 20 items was used. Non-parametric tests were used for data analysis. Mann-Whitney U was used to determine the difference between two groups, Kruskall-Wallis analysis method was used to determine the difference between more than two groups. The average of total score of Mental Training Scale of the participants was determined as X̄ = 3.97. In other words, the level of mental training of the participants was found to be high. The sub-dimension with the highest average was found to be the Interpersonal Skills sub-dimension with the average of X̄ = 4.32, and the sub-dimension with the lowest average was the Mental Performance Skills sub-dimension with the average of X̄ = 3.70. In addition, different results were determined according to gender and ritual variables.
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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 | 0.000 | 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) | 0.001 | 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".