The Determinant Effect of Self-Talk Status of Athletes on Life Satisfaction
Bibliographic record
Abstract
The main purpose of this research is to examine the effect of athletes’ self-talk over their life satisfaction. One hundred-sixteen females (age = 24.40 ± 3.65 year) and 200 males (age = 25.28 ± 3.47 year) voluntarily participated in this research. The sample of the research consists of athletes who were in different branches and have different levels of sportiveness degree and history. In this research, information about socio-demographic variables was collected by Personal Information Form which was composed by the researcher. “Self-talk Questionnaire” and “Life Satisfaction Scale” were used to reveal individual differences between the study’s variables. In the analysis of the data descriptive statistical methods and in independence examples T-test and Multiple Stepwise Regression Analysis methods were used. In the result of the present research, in addition to obtained conclusion that self-talk levels of athletes are a predictor of life satisfaction, it has been observed that self talk levels of athletes differ from each other regarding gender variable (p < 0.05). Also, it has been observed that the life satisfaction levels of athletes differ in terms of gender variable (p < 0.05). As a result, it has been thought that the relationship between life satisfaction and motivational and cognitive self-talk can be explained by self-determination theory.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".