Developing injury indicators for Canadian children and youth: a modified-Delphi approach
Bibliographic record
Abstract
OBJECTIVE: To develop a set of national injury indicators for Canadian children and youth which will eventually be used to reflect and monitor identified prevention priorities. METHODS: The Canadian Injury Indicators Development Team brought together injury researchers, policy makers, and practitioners to develop injury indicators in the following areas: overall health services implications; motor vehicle occupant; sports, recreation, and leisure; violence; and trauma care, quality, and outcomes. A modified-Delphi process was used to establish a set of indicators that met evidence-based criteria, were useful, and that would prompt action. Each indicator was rated by 132 respondent injury experts and stakeholders on its usefulness and ability to prompt action to reduce injury among Canadian children and youth. RESULTS: From an initial list of 51 indicators, a refined set of 34 indicators was established. Indicators were grouped into three categories related to: policies; risk and protective factors; and outcomes. Indicators related to motor vehicle injury were rated as most useful and most able to prompt action. Injury mortality rate and injury hospitalisation rate were also rated highly for both usefulness and ability to prompt action. Policy, violence, sport and recreation, and trauma indicators were all rated higher for usefulness, but somewhat lower for ability to prompt action. CONCLUSION: Results suggest that a broad-based modified-Delphi process is an important first step in developing useful and relevant indicators for injury prevention activity focused on Canadian children and youth.
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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 | 0.084 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".