Characteristics and risk factors for accident injury in Canada from 1986 to 1996: an analysis of the Canadian Accident Injury Reporting and Evaluation (CAIRE) database
Bibliographic record
Abstract
This study analyzed the database of Canadian Accident Injury Reporting and Evaluation (CAIRE) for the injuries reported from January 1986 to March 1996 in seven provinces at children's or general hospitals in Canada. In order to describe the characteristics of injuries, we compared the different categories of injuries by sex and by age groups, identified patterns of injuries, and detected the products causing injury to Canadian people. The results showed that there were 130,489 injury cases in Canada during the 10 years from 1986 to 1996. The 10-19 year age group had 57,582 cases, representing 44.13% of total injuries, and making it the group with the highest occurrence of injuries. The male injury rate (69.75%) was significantly higher than the female rate (30.25%) (P = 0.0001). Six areas were identified as priorities for intervention: 1) injuries occurring on playgrounds among children and youth; 2) sports and playground apparatus injuries and injuries sustained in transit among young people; 3) the top five causes of injuries; 4) diagnosis and treatment of injuries; 5) consumer products and safety; and 6) nature and physical sites of injuries. Further work is needed in: evaluating injury causes, comparing the results with reports from other countries and the necessary approaches and prevention measures to reduce and control injury occurrences to improve the quality of consumer products, and to protect the health of the population in Canada.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".