Moving from evidence to action: prioritisation of pediatric injury issues for focused injury prevention programming
Bibliographic record
Abstract
Objective To develop a prioritised, evidence-informed list of pediatric injury issues to be addressed through injury prevention initiatives. Method A quantitative review of trauma data with qualitative stakeholder discussions. Data representing all levels of severity from ED visits to deaths were reviewed and ranked by age (<1, 1–4 years, 5–9 years, 10–14 years, 15–17 years). Specific topics for each of the top issues (MVC, recreational, falls, intentional, drowning and other) were discussed based on qualitative criteria (ie, effectiveness, opportunity gaps) and individually scored on a 5-point Likert scale. The sum of these scores was ranked. This qualitative rank was added with the quantitative rank from the data to determine the overall ranking, with the lowest rank sum as the top priority. Results Shaken baby syndrome (SBS) was ranked as the top priority. The top five ranking injury issues are presented in Table 1. Overall Rank Topic Age group (year) Quant Rank + Qual Rank = Rank Sum 1 SBS <1 2 1 3 2 Bullying 5–17 yrs 3 2 5 2 Drugs & Driving 15–17 1 4 5 2 MVC & Speeding 15–17 1 4 5 5 Suicide 10–17 4 3 7 Other issues included ATV safety, playground and farm injuries. Conclusion With competing demands and limited resources, this method utilises data and stakeholder input to decide where to focus efforts. It allowed us to move from evidence to action, by implementing and evaluating new programs including SBS-prevention and unsafe driving programs.
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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 | 0.221 | 0.258 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.025 | 0.013 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.007 | 0.021 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".