COVID-19-related impact on mental health and career uncertainty in student-athletes—Data from a cohort of 7,025 athletes in an elite sport high school system in Sweden
Bibliographic record
Abstract
Objectives: Mental health consequences and behavior change has been described in elite athletes following the vast impact of the COVID-19 pandemic on the world of sports. However, most study samples have been of limited size, and few studies have assessed student-athletes. This study aimed to analyze perceived mental health impact, measured as clinical degree of depression and anxiety, worry about one's sport and about one's career, and behavioral change with respect to video gaming behavior, in high-school athletes in Sweden. Methods: = 7,025) in February 2021, during the ongoing COVID-19 pandemic. Results: Sixteen and 14% met criteria of moderate/severe depression and anxiety, respectively. Many respondents reported feeling mentally worse during the pandemic (66%), and were worried about the future of their sport (45%) or about their own future in sports (45%). Increased gaming behavior during COVID-19 was reported by 29%. All mental health variables were significantly more common in women, except increased gaming (more common in men). Being worried about one's career was less common in winter sports, more common in team sports and more common in older student-athletes, and associated with both depression and anxiety in regression analyses. Discussion: Self-reported mental health impact of COVID-19 is substantial in student-athletes, and even more so in women and in team sports. The lower impact in winter athletes suggests a moderating effect of the seasons in which the COVID-19 outbreak occurred.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".