Public Engagement in Canadian Health Policy: Looking Back, Taking Stock and Charting the Future
Bibliographic record
Abstract
are up to the task, and what adaptations or new approaches might be needed. If the persisting inequities in health systems across Canada are going to be addressed, it is imperative that those designing, developing and implementing policies find ways to reflect the needs and preferences of the communities and populations most adversely affected by these inequities in their decisions. The purpose of this special issue is to address this important topic through a series of research papers and commentaries. Our work is targeted to health policy makers across Canada who are seeking to engage with various publics on a wide array of health policy issues. We offer key insights into what more purposeful and equitable public engagement might look like, as well as common pitfalls in public engagement practices and how they can be avoided.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.093 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.023 | 0.022 |
| Scholarly communication | 0.033 | 0.012 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.035 | 0.038 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".