COVID-19: Physical Activity Behavior Change among Athletes in Québec (Canada)
Bibliographic record
Abstract
The context of the COVID-19 pandemic imposed unprecedented restrictions. Within Canada, which is among the most stringent countries in terms of sanitary rules, Québec was among the provinces that imposed the strictest sanitary measures. The impacts of some measures were felt the most among athletes since they made it difficult, if not impossible, to practice their sports. This article therefore aimed to (1) evaluate the impact of the pandemic on the athletes’ overall level of physical activity, (2) look at the relationship between overall level of physical activity and the level of psychological well-being and (3) analyze post-pandemic physical activity intentions. For this purpose, an online survey was conducted among 1456 athletes aged 3 to 61 years old. The results of this study show that the limitations imposed during the pandemic led to half of athletes decreasing their overall level of physical activity, leading to a deterioration in their psychological well-being (F(2.1438) = 54.707, p < 0.001). The current research provided further evidence that it is essential to implement strategies that favor practicing physical activities in a pandemic context. Furthermore, since almost all individuals who increased their practice of wheeled sports during the pandemic intend to continue after the pandemic, this is a great opportunity to promote active transportation among athletes by ensuring that the perception of the benefits associated with it does not decrease with time.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".