Developing injury indicators for First Nations and Inuit children and youth in Canada: a modified Delphi approach
Bibliographic record
Abstract
INTRODUCTION: The purpose of this research was to take the initial step in developing valid indicators that reflect the injury issues facing First Nations and Inuit children and youth in Canada. METHODS: Using a modified-Delphi process, relevant expert and community stakeholders rated each indicator on its perceived usefulness and ability to prompt action to reduce injury among children and youth in indigenous communities. The Delphi process included 5 phases and resulted in a refined set of 27 indicators. RESULTS: Indicators related to motorized vehicle collisions, mortality and hospitalization rates were rated the most useful and most likely to prompt action. These were followed by indicators for community injury prevention training and response systems, violent and inflicted injury, burns and falls, and suicide. CONCLUSION: The results suggest that a broad-based modified-Delphi process is a practical and appropriate method, within the OCAP™ (Ownership, Control, Access and Possession) principles, for developing a proposed set of indicators for injury prevention activity focused on First Nations and Inuit children and youth. Following additional work to validate and populate the indicators, it is anticipated that communities will utilize them to monitor injury and prompt decisions and action to reduce injuries among 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 | 0.052 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 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".