The Effects of the COVID-19 Pandemic on Physical Activity Behaviours and The Implications for Health and Wellness
Bibliographic record
Abstract
Description: This symposium chaired by Dr. Shannon Bredin, University of British Columbia, will explore the current evidence regarding the effects of the COVID-19 pandemic on physical activity behaviours and the implications for health and well-being in diverse populations. Dr. Jack Taunton, Professor Emeritus, UBC and Allan McGavin Sports Medicine Centre, will discuss the treatment of persons exposed to COVID-19 with particular emphasis on those with the Long Hauler Syndrome. Dr. Paul Oh, Toronto Rehabilitation Institute, will highlight the important role cardiac rehabilitation approaches will play in the treatment of those living with the long-term effects of COVID-19 infection. Dr. Jodi Edwards, University of Ottawa Heart Institute, will provide a patient's perspective living with and recovering from COVID-19. Dr. Simon Bacon, Concordia University, will discuss the lessons learned from global initiatives, such as the International Assessment of COVID-19-related Attitudes, Concerns, Responses and Impacts in Relation to Public Health Policies (iCARE) study. The session will end with a discussion of Public Health Agency of Canada funded projects and the changes that were made to deal with the effects of the COVID-19 pandemic on physical activity programming at the community level.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".