Competitive Sports Anxiety among Nepalese National Athletes: Differences by Gender, Sport Type, and Sporting Disciplines
Bibliographic record
Abstract
Sport psychology focuses on the influence of competitive anxiety on performance, despite a paucity of work with athletes in Nepal. The prevalent patterns of sports competition anxiety based on gender, sports type and sporting disciplines are examined in this study. The sample consisted of 496 athletes (281 males and 215 females) who participated in 21 events from the 9th National Games in Pokhara.Competitive anxiety was measured using the Sports Competition Anxiety Test (SCAT). Nonparametric statistical techniques were used as the anxiety scores violated normality assumptions. A Mann–Whitney U test indicated male and female athletes did not show statistically significant differences in anxiety (U = 29701.50, p = .749, r = 0.014). However, athletes competing in individual sportsreported significantly higher anxiety than compared to team events (U = 25813, p = .002, r = 0.14). A Kruskal–Wallis test revealed significant differences in anxiety across the 21 sports examined (H = 134.87, p < .001, η² = .27). Kabaddi, Kho- Kho and Archery athletes had the highest anxiety levels, while Football, Rugby and Volleyball athletes had the lowest ones. This implies that competitive anxiety in the athletes of Nepal is more affected not by gender but by sport type and sport-specific demands. The findings emphasize the requirement for targeted psychological assistance, especially for individual and precision-based sportsmen.
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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 | 0.001 | 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 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".