Psychological Distress and Problem Gambling in Elite Athletes during COVID-19 Restrictions—A Web Survey in Top Leagues of Three Sports during the Pandemic
Bibliographic record
Abstract
COVID-19 and lockdown strategies may affect mental health and addictive behavior differently in the population, and elite athletes are among the professions clearly affected by the pandemic. This study in top elite athletes aimed to study current perceived psychological influence from COVID-19 and symptoms of depression, anxiety and changes in alcohol drinking, gambling behavior and problem gambling in the midst of the COVID-19 lockdown. This web survey included athletes in top leagues of soccer, ice hockey and handball in Sweden (N = 327, 62% men). A total of 66% and 51% were worried about the future of their sport or about their own future in sports, respectively. Feeling worse psychologically during the pandemic was common (72% of women, 40% of men, p < 0.001); depression criteria were endorsed by 19% of women and three percent of men (p < 0.001); anxiety criteria by 20% of women and five percent of men (p < 0.001). Reporting increased gambling during the pandemic was associated with gambling problem severity. Moderate-risk or problem gambling was seen in 10% of men and none of the women (p < 0.001). Depression and anxiety were associated with feeling worse during the COVID-19 pandemic and with concern over one’s own sports future. In conclusion, COVID-19-related distress is common in elite athletes and associated with mental health symptoms. Gambling increase during the pandemic was rare, but related to gambling problems, which were common in male athletes. The calls for increased focus on COVID-19-related concerns in athletes and on problem gambling in male athletes.
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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) | 0.001 | 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".