Assessing the representativeness of Canadian Hospitals Injury Reporting and Prevention Programme (CHIRPP) sport and recreational injury data in Calgary, Canada
Bibliographic record
Abstract
The objective of this study was to assess the representativeness of sport and recreational injury data from Canadian Hospital Injury Reporting and Prevention Programme (CHIRPP) in Calgary. Internal representativeness was assessed by comparing CHIRPP and regional health administrative data (ambulatory care classification system-ACCS) at Alberta Children's Hospital (ACH). External representativeness was assessed by comparing CHIRPP with ACCS at all hospitals. Comparisons were performed using descriptive statistics for top injury-producing sports and sports that produced severe injuries. Stratified distributions of injury-producing sports by gender, age group and severity of injury in CHIRPP and ACCS were compared. The proportion of all injuries in Calgary captured by CHIRPP was 64.8% (99%CI: 64.02-65.54%) (16,977/26,206). CHIRPP captured more cases of top injury-producing sports than ACCS at ACH. Rankings of top injury-producing sports in CHIRPP and ACCS at ACH were remarkably consistent (ρ = 0.92, p < 0.0001). Rankings of top injury-producing sports in CHIRPP and ACCS at all hospitals were almost identical (ρ = 0.98, p < 0.0001). Stratified distributions of top injury-producing sports by gender, age group and the severity of injury showed strong consistency between CHIRPP and ACCS. It is concluded that CHIRPP in Calgary provides a representative profile of injuries compared to regional health administrative data. This project supports the use of CHIRPP for establishing injury prevention priorities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".