Hydration Status and Self-Esteem as Predictors of Athlete Burnout: A Cross-Sectional Study
Bibliographic record
Abstract
Objective: This study aims to explore the relationship between athlete burnout, hydration status, and self-esteem. It hypothesizes that both hydration status and self-esteem significantly predict athlete burnout. Methods and Materials: The study employed a cross-sectional design involving 230 athletes from various sports clubs and institutions. Participants were actively engaged in competitive sports for at least one year, aged between 18 and 35 years, and free from chronic health conditions. Athlete burnout was measured using the Athlete Burnout Questionnaire (ABQ), hydration status was assessed using the Urine Specific Gravity (USG) test, and self-esteem was evaluated using the Rosenberg Self-Esteem Scale (RSES). Data analysis involved Pearson correlation coefficients to assess relationships and multiple linear regression to evaluate predictive values. Results: Descriptive statistics indicated a mean score for athlete burnout of 3.10 (SD = 0.65), a mean USG for hydration status of 1.025 (SD = 0.004), and a mean score for self-esteem of 21.85 (SD = 4.72). Correlation analysis showed a positive correlation between athlete burnout and hydration status (r = 0.32, p < .001), a negative correlation between athlete burnout and self-esteem (r = -0.45, p < .001), and a weak negative correlation between hydration status and self-esteem (r = -0.15, p = .021). Regression analysis revealed that hydration status and self-esteem together explained 29% of the variance in athlete burnout, with both being significant predictors. Conclusion: The findings suggest that higher levels of dehydration and lower self-esteem are associated with increased athlete burnout. Ensuring adequate hydration and fostering self-esteem are crucial for reducing burnout. Interventions should focus on maintaining hydration, enhancing self-esteem, and promoting self-determined motivation.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".