Impact Of Covid-19 Pandemic On Injury And Illness In Canada Games Competition
Bibliographic record
Abstract
Participation in elite level competition, such as Canada Games (CG), encourages large scale participation in Summer and Winter sport events; however, there are inherent injury risks, which may have been magnified by activity restrictions imposed during the COVID-19 pandemic lockdown. PURPOSE: To compare if injury/illness characteristics, incidence, and odds of injury for 2022 CG were different from pre-pandemic CG. METHODS: De-identified data from 2022 and pre-pandemic CG (2009 - 2019) were categorized based on injury area and type, and acute/chronic injury; illness was categorized by affected system. Frequency of injured body area, type, acute/chronic, and illness were calculated as a percentage of total injuries/illnesses. Incidence of injury/illness was calculated per 1000 athletes. Odds ratio (OR [95% CI]) for injury/illness were calculated for differences between 2022 and pre-pandemic CG. Microsoft Excel was used for analysis with p < 0.05 for statistical significance. RESULTS: There were 1955 male (M) and 1786 female (F) athletes participating in 2022 CG; pre-pandemic CG averaged 1819 M and 1571 F athletes. In 2022 CG, thigh was most frequently injured (n = 151; 12.3%), strains were most common (n = 538; 46.9%), most injuries were overuse (n = 664; 57.8%), and other category was most often affected illness system (n = 38; 34.2%). In pre-pandemic CG, shoulder was most frequently injured (n = 368; 10.0%), sprains were most common (n = 1444; 33.7%), most injuries were acute (n = 1854; 50.6%), and other category was most often affected illness (n = 142; 29.5%). Injury incidence was 306.87 and 360.31 and illness incidence was 29.67 and 47.30 per 1000 athletes in 2022 CG and pre-pandemic CG, respectively. Athletes competing in 2022 CG had significantly lower odds of injury (OR = .79 [.73 - .85]) and illness (OR = .62 [.50 - .76]) compared to pre-pandemic CG. CONCLUSION: 2022 CC participants had lower incidence and odds of injury/illness than pre-pandemic CG; however, there were differences in injury characteristics. Suggesting that, although pandemic restrictions may have had a protective effect, there were more chronic injuries, and the body area and injury type differed from pre-pandemic CG. Supported by a Brock University Canada Games Grant; Acknowledgement: Canada Games Council for providing de-identified data
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".