The Canadian Atlas of Child and Youth Injury: Mobilizing Injury Surveillance Data to Launch a National Knowledge Translation Tool
Bibliographic record
Abstract
Child and youth injury prevention research in Canada has lagged behind other Organisation for Economic Co-operation and Development nations, despite existing surveillance systems and longitudinal data. A critical need to improve access to the available data, as well as need to tailor its display and interpretation, was identified by injury prevention stakeholders involved in research, policy, and practice. The Canadian Atlas of Child and Youth Injury Prevention ("the Atlas") was developed to address this need. Following a series of iterative consultation meetings and a pilot testing session, the Atlas was scaled up with national data. Two testing sessions were held to evaluate the tools. The Atlas is comprised of three main components: data, indicators, and visualizations. The accessibility of the dashboard is enhanced by customization of data visualizations and data outputs to suit the user's needs. Overall feedback indicated that the tools were easy to use, and that the interface was intuitive and visually appealing. The Canadian Atlas of Child and Youth Injury Prevention provides readily accessible information to injury prevention practitioners, policy makers and researchers, helping to chart pathways to success in improving the child and youth injury prevention system 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 | 0.016 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".