Injuries and Illnesses Across 10 Years of Canada Games Competitions: 2009 – 2019
Bibliographic record
Abstract
Background: The Canada Games are a national level competition held every two years alternating between Summer and Winter Games. Participation in elite level athletics, like the Canada Games, have an inherent risk of injury and illness. Purpose: To analyze the incidence and characteristics of injuries and illnesses during Canada Games competitions from 2009-2019 (primarily) and to understand sex differences in odds of musculoskeletal injury for Summer and Winter Canada Games athletes (secondarily). Study Design: Descriptive Epidemiology Study. Methods: Using a retrospective cohort, data were abstracted from medical incident reports generated during Canada Games from 2009 - 2019. Data were coded for body part injured and injury type or illness system; injuries were also categorized as acute or chronic. Results: Across all 10 years of competition, 3160 injuries reported in 8710 male athletes and 3272 injuries reported in 8391 female athletes. Injury incidence was 362.8 and 389.9 and illness incidence was 47.8 and 64.5 per 1000 male and female athletes, respectively. Female athletes had a 1.12 (95% CI: 1.06; 1.19) greater odds of injury and 1.37 (95% CI: 1.20; 1.57) greater odds of illness compared to male athletes. Overall, injury (399.31 vs. 360.31; p < 0.001) and illness (68.67 vs. 47.30; p < 0.001) incidences were higher in Winter Games, compared to Summer Games, per 1000 athletes. When comparing male and female athletes participating in similar sports, sex specific differences exist in odds of both injury and illness. Conclusions: Male and female athletes competing in Canada Games competitions demonstrate differences in injury and illness incidence and odds of injury. This suggests a need to examine if additional modifiable risk factors may exist, which could contribute to prevention strategies to reduce injury and illness during Canada Games competition. Level of Evidence: 3.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".