The economic burden of athletic injuries across 10 years of Canada Games
Bibliographic record
Abstract
Injuries in elite sports are responsible for a substantial economic burden on host organizations, requiring informed decisions to ensure that injury treatment is delivered in an efficient manner. However, there is a paucity of economic assessments that have been conducted surrounding elite sport injury events in Canada. Objective: To estimate the direct medical and opportunity costs of treating various injuries by volunteer medical professionals at the Canada Games (CG). Method: A decision tree model (DTM) incorporated parameters on injury treatment lengths as estimated from a Delphi survey, injury surveillance data from past CG competitions (2009-2019), institutional spending derived from the Athlete Medical Program, and fee-for-service rates for medical professionals derived from government reports. Expected costs were calculated using probabilities from logistic regression analyses and reported in Canadian Dollars as of 2023. A one-way deterministic sensitivity analysis was undertaken which varied annual spending by ±10%. Results: There were 15,717 injury events reported during initial and follow-up visits at on-site polyclinics between the 2009 and 2019 CG events (6 competitions). Median estimated treatment lengths during initial visits were highest for patellofemoral pain syndromes (30.0 [IQR = 15.0-33.5] minutes) and were highest during follow-up visits for impingement injuries (25.0 [IQR = 14.0-30.0] minutes). Knee, ankle, lumbar, shoulder/clavicle, and thigh injuries, had a cumulative average expected medical cost of about $103, $113, $383, $417, and $172, respectively, per event. After having incurred a knee injury, the average opportunity cost of being treated by a physician, a combination of athletic therapist/physiotherapist, or a combination of massage therapist/chiropractor, were $17, $184, and $35, respectively. 72% of athletes treated by a physician were referred for follow-up care. Overall, the total expected medical and opportunity cost of athletic injuries in CG per year were $156,620 and $378,574, respectively. Conclusion: This thesis reports on estimated medical and opportunity costs associated with treating various athletic injuries at on-site polyclinics at CG. Findings from our study can inform decision-making in medical management to support treatment protocol reform, while also informing economic evaluations of future CG or other elite sport competitions. More research is needed to estimate the broader health system and out-of-pocket costs of athletic injuries at elite sporting events beyond on-site polyclinics.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".