How did Canadian national team athletes manage the COVID-19 pandemic? Athlete, coach, and support staff perspectives to guide future responses to major stressors
Bibliographic record
Abstract
As we move beyond the pandemic and the lockdowns it imposed on elite athletes, there remains much to learn from how they adapted to this unprecedented crisis. Given the significant impact on their mental health-and the likelihood that they will face other major stressors throughout their careers-this study explored the interplay between the COVID-19 pandemic, mental health, and mental performance among Canadian national team athletes. The perspectives of athletes, coaches, and support staff were examined through focus groups and interviews conducted during the second wave of the pandemic in Canada. Participants included 25 athletes, eight coaches, and five support staff members. Inductive reflexive thematic analysis generated three main themes: (a) consequences of COVID-19 for athletes, (b) factors influencing athlete mental health, and (c) coach and support staff perspectives on well-being and evolving roles. Athletes reported a range of experiences influenced by factors such as isolation, stigma, coping skills, and social support. Mental health and mental performance emerged as core components of a culture of excellence-interrelated and mutually reinforcing. These findings underscore the importance of fostering environments that prioritize well-being alongside performance, particularly as sport organizations prepare for future periods of uncertainty and stress.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.036 | 0.010 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".