Strategies for engaging policy stakeholders to translate research knowledge into practice more effectively: Lessons learned from the Canadian Alcohol Policy Evaluation project
Bibliographic record
Abstract
INTRODUCTION: Evidence-based alcohol policies have the potential to reduce a wide range of related harms. Yet, barriers to adoption and implementation within governments often exist. Engaging relevant stakeholders may be an effective way to identify and address potential challenges thereby increasing reach and uptake of policy evaluation research and strengthening jurisdictional responses to alcohol harms. METHODS: As part of the 2019 Canadian Alcohol Policy Evaluation project, we conducted interviews with government stakeholders across alcohol-related sectors prior to a second round of researcher-led policy assessments in Canada's 13 provinces and territories. Stakeholders were asked for feedback on the design and impact of an earlier policy assessment in 2013 and for recommendations to improve the design and dissemination strategy for the next iteration. Content analysis was used to identify ways of improving stakeholder engagement. RESULTS: We interviewed 25 stakeholders across 12 of Canada's 13 jurisdictions, including representatives from government health ministries and from alcohol regulation, distribution and finance departments. In providing feedback on our stakeholder engagement strategy, participants highlighted the importance of maintaining ongoing contact; presenting results in accessible online formats; providing advance notice of results; and offering jurisdiction-specific webinars. DISCUSSION AND CONCLUSIONS: This study offers important insight into the engagement preferences of government stakeholders involved in the health, regulation, distribution and financial aspects of alcohol control policy. Findings suggest that seeking input from stakeholders as part of conducting evaluation research is warranted; increasing the relevance, reach and uptake of results. Specific stakeholder engagement strategies are outlined.
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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 | 0.546 | 0.400 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.036 | 0.024 |
| Scholarly communication | 0.027 | 0.014 |
| Open science | 0.012 | 0.030 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".