Youth injury data in the Canadian Hospitals Injury Reporting and Prevention Program: do they represent the Canadian experience?
Bibliographic record
Abstract
OBJECTIVE: Injuries to Canadian youth (11-15 years) identified from a population based health survey (World Health Organization-Health Behaviour in School-Aged Children Survey, or WHO-HBSC) were compared with youth injuries from a national, emergency department based surveillance system. Comparisons focused on external causes of injury, and examined whether similar rankings of injury patterns and hence priorities for intervention were identified by the different systems. SETTING: The Canadian version of the WHO-HBSC was conducted in 1998. The Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP) is the national, emergency room based, surveillance program. Two hospitals involved in CHIRPP collectively provide population based data for Kingston, Ontario. METHOD: Numbers of injuries selected for study varied by data source: WHO-HBSC (n=3673); CHIRPP (n=20,133); Kingston CHIRPP (n=1944). WHO-HBSC and Kingston CHIRPP records were coded according to four variables in the draft International Classification of External Causes of Injury. Existing CHIRPP codes were available to compare Kingston and other CHIRPP data by five variables. Males and females in the three datasets were ranked according to the external causes. Data classified by source and sex were compared using Spearman's rank correlation statistic. RESULTS: Rank orders of four variables describing external causes were remarkably similar between the WHO-HBSC and Kingston CHIRPP (p>0.78; p<0.004) for mechanism, object, location, and activity). The Kingston and other CHIRPP data were also similar (p>0.87; p<0.001) for the variables available to describe external causes of injury (including intent). CONCLUSION: The two subsets of the CHIRPP data and the WHO-HBSC data identified similar priorities for injury prevention among young people. These findings indicate that CHIRPP may be representative of general youth injury patterns in Canada. Our study provides a novel and practical model for the validation of injury surveillance programs.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.018 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".