Federal Injury Surveillance in Canada: Filling the Gaps
Bibliographic record
Abstract
Health Canada's experience in injury epidemiology was almost nonexistent when, in May, 1989, representations to the Deputy Minister resulted in the formation of a 10-hospital surveillance system for childhood injury on a three year pilot basis. The first of the three years was devoted to investigating injury surveillance systems around the world for philosophical and technical merit and negotiating a working arrangement with the 10 Canadian pediatric hospitals. Eleven months later, in April, 1990, the first data from the Children's Hospitals Injury Reporting and Prevention Program (CHIRPP) were generated. CHIRPP was based on the Australian national injury surveillance program (NISPP) and although a number of modifications have been made in both programs over the last four years they continue to share almost identical data collection strategies and record content. Both are Emergency Room-based systems which emphasize pre-injury event circumstances, as small a response burden on data providers as possible, a rapid processing turnaround for timeliness and a powerful software interface which is given to all program participants. CHIRPP is now the Canadian Hospitals... Program because it now includes five general hospitals and has become, by default, an all-ages surveillance program although the emphasis remains on children. The presentation will concentrate on the strengths of the ER-based approach, some of the major difficulties that have been encountered
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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 | 0.029 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.017 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".