Abstract 1: Mental health of NextGen student-athletes: How are they doing at college and university?
Bibliographic record
Abstract
NextGen student-athletes experience multiple stressors that are present in both in sport and in school context, including training in highly demanding environment while also receiving little financial support from their sports federations and balancing two schedules (school and training) that are independent to one another. While many studies have explored symptoms of mental disorders among student-athletes, little is known regarding the NextGen student-athletes’ population. The purpose of the current study was to 1) identify the proportion of symptoms of mental disorders (depression, eating disorders and anxiety) and the level of well-being of NextGen student-athletes and 2) to compare the proportion of these symptoms of mental disorders and the level of well-being in NextGen college student-athletes and NextGen university student-athletes. 184 NextGen student-athletes completed an online survey. The results revealed that 20.7% of NextGen student-athletes met the criteria for depressive symptoms, 7.1% for eating disorders symptoms, and 19.0% for anxiety symptoms. 51.1% reported a low to moderate level of well-being. NextGen university student-athletes showed significantly higher levels of well-being than NextGen college student-athletes, while NextGen college athletes reported significantly more depressive symptoms. College athletes are typically younger and may be less equipped with coping strategies compared to university athletes. These findings underscore the need for targeted mental health interventions and support systems tailored to the specific needs of NextGen student-athletes, especially those at the collegial level. Schools and sports federations should consider implementing programs to address mental health and develop coping strategies to help these athletes manage their dual responsibilities effectively.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".