Trends in Government-Initiated Public Engagement in Canadian Health Policy From 2000 to 2021
Bibliographic record
Abstract
Introduction: Canada has a rich history of public engagement in health policy; however, shifts in engagement practices over time have not been critically examined. Methodology: We searched for cases of government-initiated public engagement in Canadian health policy from 2000 to 2021 at the federal, provincial (Ontario, British Columbia, Nova Scotia) and pan-Canadian levels. Government databases, portals and platforms for engagement were searched, followed by academic and grey literature using relevant search terms. A coding scheme was iteratively developed to categorize cases by target population, recruitment method and type of engagement. Results: We identified 132 cases of government-initiated public engagement. We found a predominance of feedback and consultation engagement types and self-selection recruitment, especially at the federal level from 2016 onward. Engagements that targeted multiple populations (patients, public and other stakeholders) were favoured overall and over time. Just over 10% of cases in our survey mentioned efforts to engage with equity-deserving groups. Conclusion: Overall, our results identify a heavy reliance over time on more passive, indirect engagement approaches, which limit opportunities for collaborative problem solving and fail to include equity-deserving populations. Those overseeing the design and implementation of government-initiated public engagement will draw valuable lessons from this review to inform the design of engagement initatives.
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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 | 0.014 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.027 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".