Do Female University Varsity Athletes Have a Greater Risk of Injury Within a Competitive Varsity Season?
Bibliographic record
Abstract
Previous varsity sport injury research has analyzed how acute and chronic injury severity, type, and location differs between sport and sexes, with limited research in time to injury. Canadian university varsity sport injury research is especially sparse and mostly retrospective. Thus, we aimed to understand injury differences in male and female competitive university athletes competing in the same sport. Athletes who competed on the basketball, volleyball, soccer, ice hockey, football (male), rugby (female), and wrestling teams were eligible for the study. There were 182 male and 113 female athletes who provided informed consent to be prospectively followed over a season. Injury date, type, location, chronicity, and events missed due to injury were recorded on a weekly basis. Overall, the percentage of male (68.7%) and female (68.1%) athletes injured was not different. No overall sex differences (variables collapsed) were observed in injury chronicity, location, type, events lost, mean number of injuries, or time to injury. Within sport differences existed for mean number of injuries, injury location, type of injury, and events missed. Mean time to injury in female basketball (28 days) and volleyball athletes (14 days) was significantly shorter compared to male basketball (67 days) and volleyball (65 days). Time to a concussion was significantly shorter in females overall compared to males. These results indicate that Canadian female university age athletes are not inherently more susceptible to injury, but female athletes within certain sports may have increased injury risk which could shorten time to injury (basketball, volleyball) and increase the number of events missed due to injury (hockey).
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".