The development of a framework to integrate evidence into a national injury prevention strategy
Bibliographic record
Abstract
BACKGROUND: Injury is the leading cause of death from birth to age 34 in Canada (Statistics Canada, 2008). In 2013, a national injury prevention organization in Canada initiated a research-practitioner collaboration to establish a framework for incorporating evidence in the organization's decision-making. In this study, we outline the development process and provide an overview of the framework. METHODS: The process of development of the evidence-synthesis framework included consultation with national and international injury prevention experts, a review of the research literature to identify existing models for incorporating research evidence into public health practice and extensive interactions with the organization's leadership and staff. RESULTS: A framework emphasizing four types of research evidence was recommended: (i) epidemiologic evidence describing the burden and cause of injury, (ii) evidence concerning the effectiveness of interventions, (iii) evidence on effective methods for implementing promising interventions at a population level, and (iv) evidence and theory from the behavioral sciences. Through the evidence-synthesis process the framework prioritizes highly synthesized evidence-based strategies and draws attention to important research gaps. CONCLUSIONS: This study describes a novel opportunity to operationalize an organization's commitment to integrate evidence into practice. The framework provides guidance on how to use evidence strategically to maximize the potential impact of prevention efforts. Opportunities for further evaluation and dissemination are discussed.
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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 | 0.418 | 0.254 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.043 | 0.016 |
| Science and technology studies | 0.014 | 0.040 |
| Scholarly communication | 0.032 | 0.029 |
| Open science | 0.015 | 0.028 |
| Research integrity | 0.021 | 0.023 |
| Insufficient payload (model declined to judge) | 0.005 | 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".