Minds matter: how COVID-19 highlighted a growing need to protect and promote athlete mental health
Bibliographic record
Abstract
Mental health symptoms are common among professional, Olympic/Paralympic and collegiate athletes, with prevalence rates (15%-35%) equivalent to or exceeding those of non-athletes.1 Mental health symptoms are also common among youth and adolescent athletes, with a prevalence of up to one-third in some samples.2 Recent epidemiological evidence collected during the COVID-19 pandemic suggests increased rates of mental health symptoms among athletes during lockdown.3 In professional football (soccer), the prevalence of anxiety and depression doubled during the COVID-19 emergency period compared with immediately prior in both females (N=600; 18% vs 8% for anxiety) and males (N=1309; 13% vs 6% for depression).4 A significant difference was also found in US professional endurance athletes (N=114; 27% vs 5% for feeling anxious; 22% vs 4% for feeling depressed),5 as well as in the top leagues of Swedish football, ice hockey and handball (N=327), all correlated with COVID-19 pandemic distress.3 In Norway, symptoms of insomnia (38.3%) and depression (22.3%) were common among female and male elite athletes during COVID-19 (n=378).3 Among US high school athletes, the prevalence of moderate to severe depression more than tripled during the COVID-19 emergency period compared with years prior in both female (N=1877; 37% vs 11%) and male athletes (N=1366; 27% vs 6%).6 Increased mental health symptoms among athletes in the aforementioned studies might be linked to a range of pandemic-associated factors.7 Of concern, while athletes who have been able to return to sports participation after the end of the emergency period have shown some improvement in mental health, in many cases their mental health has not fully recovered to prepandemic status.7
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".