A - 52 Post-Pandemic Mental Health Symptom Presentations In Canadian Varsity Athletes
Bibliographic record
Abstract
Abstract Purpose Mental health (MH) symptoms are often intertwined with concussion symptoms and affect recovery trajectories. Recent studies indicate that the COVID-19 pandemic had a negative impact on athletes’MH, further highlighting the need to understand pre-season baseline MH. This study’s objective was to compare MH symptom frequency and profiles of symptomatology between cohorts of athletes captured pre and post COVID-19. Method Pre-season baseline data from varsity athletes at a Canadian university was collected in 2019, 2022, and 2023 (n = 444) aged 17–25 years (49.8% female). Athletes completed a series of demographic, concussion history, and psychological questionnaires [i.e., Post-Concussion Symptom Scale (PCSS), Generalized Anxiety Disorder Index-7 (GAD-7), and Patient Health Questionnaire-9 (PHQ-9)]. Results The post-pandemic cohort reported greater numbers of symptoms on the PCSS (M = 4.36, SD = 5.41) compared to the pre-pandemic cohort (M = 2.55, SD = 3.74; p = <0.001); as well as greater symptom severity (M = 7.75 SD = 11.45) compared to the pre-pandemic cohort (M = 4.34, SD = 7.9; p = <0.001). Greatest endorsement differences were observed in anxiety, depression, cognitive, nausea, and “don‚Äôt feel right”symptoms (ps < 0.001). However, frequency of mild to severe symptom reporting was similar on the PHQ-9 (pre-pandemic: 23.6%, post-pandemic: 20.3%) and GAD-7 (pre-pandemic: 20.9%, post-pandemic: 21.6%; ps >0.05). Conclusions Post-pandemic athletes appear to endorse greater baseline symptoms of anxiety and depression, as well as cognitive and physical symptoms, though these do not appear to meet clinical levels. The study demonstrates a need to improve surveillance in varsity athletes’MH that may help identify athletes at risk of MH illness.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".