Anxiety and Psychological Well-Being Levels of Faculty of Sports Sciences Students
Bibliographic record
Abstract
The aim of the study is to examine the anxiety and psychological well-being levels of the students of the sports sciences. The study is in relational screening model. 210 university students participated in the study with simple random sampling method. Collection of data; Anxiety assessment scale and psychological well-being scale were used. Information was collected from the participants about gender, whether to do active sports or not, and income level. Data analysis was done with SPSS 22 package program. Skewness and kurtosis values are in normal distribution. Independent sample t test was used in binary comparisons and Anova test was used in more than two comparisons. Pearson correlation test was used in the correlation analysis. The significance level for all tests is α = 0.05. Participants have moderate anxiety. According to gender, women’s anxiety level is higher than men’s. There was a significant difference in anxiety psychic subscale and total score. In anxiety, somatic sub-dimension and psychological well-being, no significant difference was found in terms of gender. (P<0.05). According to whether to do active sports; There is no significant difference for anxiety and psychological well-being (p>0.05). According to the economic level; Significant differentiation was found in anxiety somatic sub-dimension and total score. Those with bad income were found to have high anxiety and low psychological well-being than those with good and moderate levels (p<0.05).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".