The progression and mechanisms of mental illness symptoms in university student‐athletes during the COVID‐19 pandemic
Bibliographic record
Abstract
A few studies have examined mental illness symptoms in university student-athletes during the COVID-19 pandemic, but the limited use of longitudinal design limits the understanding of the progression of these symptoms and the mechanisms by which they developed. The present research aims to describe the trajectory of variation of mental illness symptoms (anxiety, depression, alcohol consumption disorders, and eating disorders) throughout the pandemic, to test causality between perceived stress and symptoms, and to identify individual characteristics (sociodemographic, perceived stress, and social support) influencing the trajectories of mental illness symptoms. On three occasions during the pandemic, 211 university student-athletes were surveyed. Latent growth models and random intercept crossed-lagged panel models were performed. Results indicate that anxiety and depressive symptoms significantly decreased throughout the COVID-19 pandemic while alcohol consumption disorder symptoms significantly increased and eating disorder symptoms did not change significantly. Second, perceived stress was a significant cause of anxiety and depressive symptoms during this pandemic. Conversely, eating disorder symptoms significantly predicted perceived stress. Finally, average perceived stress and average social support availability throughout the COVID-19 pandemic, and identifying as a visible minority, significantly predicted the trajectory of depressive symptoms, allowing for the identification of a sub-population at higher risk. Based on these findings, teaching stress management strategies should be an essential component of programs to prevent mental illness symptoms in university student-athletes. Reducing environmental stressors and their consequences among this population should also be prioritised.
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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 | 0.000 | 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".