079 Sport-related injury in high school students: checking in after a decade of injury prevention interventions
Bibliographic record
Abstract
Background In 2004, a survey conducted in Alberta, Canada (n=2850) reported that 93.8% of high school students (ages 14–19) participated in sport over the previous year with injury rates (IR) of 65.7 injuries/100 students/year, 40.2/100 students/year for injuries requiring medical attention and 49.9 injuries/100 students/year for time loss injuries. Over the past decade, the Sport Injury Prevention Research Centre has introduced injury prevention programs to decrease the risk of sport-related injury among adolescents in schools and the community. Objective To examine sport participation and injury rates in high school students. Design Cross-sectional survey. Setting High schools (Alberta, Canada). Participants High school students (n=2029; 958 male, 1048 female, 23 identified as ‘other’) from 24 of 63 (38%) schools targeted for recruitment. Assessment of Risk Factors Students completed a web-based survey during class (October 2018 –March 2019). Students identified the top 3 sports for participation in the past year. Main Outcome Measurements Self-reported IR for 1) any sport-related injury over the last year, 2) most serious injury resulting in medical attention, and 3) most serious injury resulting in being restricted from sport ≥ one day adjusting for cluster by school. Results Of the 2029 respondents, 861/958 (89.9%) males, 886/1048 (84.5%) females and 16/23 (69.6%) of those who identified as ‘other’, participated in a sport/recreational activity in the last year. Of the 1971 students who completed the question on sport injury, 892 reported at least one injury over the last year (IR=45.3 injuries/100 students/year (95%CI, 40.1–50.8). The IR including only injuries resulting in medical attention was 27.8 injuries/100 students/year (95%CI, 23.4, 32.7) and resulting in time loss from sport was 35.9 injuries/100 students/year (95%CI, 40.4–41.8). Conclusions The sport-related injury rate for adolescents in Alberta is lower than previously reported 10 years ago. While, the decrease may be associated with wide scale injury prevention initiatives, it may also be related to a decline in sport participation. Future studies evaluating injury prevention strategies broadly are necessary.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".