Development of Policy-Relevant Indicators for Injury Prevention in British Columbia by the Key Decision-Makers
Bibliographic record
Abstract
Indicators can help decision-makers evaluate interventions in a complex, multi-sectoral injury system. We aimed to create indicators for road safety, seniors falls, and ‘all-injuries’ to inform and evaluate injury prevention initiatives in British Columbia, Canada. The indicator development process involved a five-stage mixed methodology approach, including an environmental scan of existing indicators, generating expert consensus, selection of decision-makers and conducting a survey, selection of final indicators, and specification of indicators. An Indicator Reference Group (IRG) reviewed the list of indicators retrieved in the environmental scan and selected candidate indicators through expert consensus based on importance, modifiability, acceptance, and practicality. Key decision-makers (n = 561) were invited to rank each indicator in terms of importance and actionability (online survey). The IRG applied inclusion criteria and thresholds to survey responses from decision-makers, which resulted in the selection of 47 road safety, 18 seniors falls, and 33 all-injury indicators. After grouping “like” indicators, a final list of 23 road safety, 8 seniors falls, and 13 all-injury indicators were specified. By considering both decision-maker ranking and expert opinion, we anticipate improved injury system performance through advocacy, accountability, and evidence-based resource allocation in priority areas. Our indicators will inform a data management framework for whole-system reporting to drive policy and funding for provincial injury prevention improvement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".