Analysis on Stress Levels of Hearing-Impaired and Physically Handicapped Sportspeople Based on Certain Demographic Variables
Bibliographic record
Abstract
The purpose of this study is to analyze the stress levels of hearing-impaired and physically handicapped elite licensed sportspeople at the national team level. Population of the research has been licensed handicapped sportspeople in Central Anatolia, and the sample group has consisted of 100 participants from this population, who were selected through the convenience sampling method. The questionnaire used in this research consists of two parts. The first part is comprised of demographics questions for determining personal information. In the second part, a stress scale has been used so that stress levels experienced with teammates and trainers can be determined. It has been adapted from the scale used by Yalçın (2009). The scale consists of 10 statements for determining the stress level. These statements were prepared based on the 5-point Likert scale, sorted as “never=1, rarely=2, sometimes=3, often=4, very often=5”. And reliability of the data has been tested with Cronbach’s Alpha coefficient (α=0.68). Normality distribution tests, the Kolmogorov-Smirnov and Shapiro-Wilk tests, have been carried out in order to determine whether the data initially presented a normal distribution at the analysis stage. The data were determined to have a normal distribution, and within this purpose, Independent Samples t Test for paired comparisons and One-Way Anova test for multiple comparisons were conducted. As a result, it has been determined that sports affect stress levels positively, there is a significant difference in stress levels of hearing-impaired and physically handicapped sportspeople and there is no significant difference between gender and marital status, educational background and disability.
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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 | 0.000 |
| 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) | 0.002 | 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".