Supporting Patient and Family Engagement for Healthcare Improvement: Reflections on “Engagement-Capable Environments” Pan-Canadian Learning Collaboratives
Bibliographic record
Abstract
Although the involvement of patients in their care has been central to the concept of patient-centred care, patient engagement in the realms of health professional education, policy making, governance, research and healthcare improvement has been rapidly evolving in Canada in the past decade. The Canadian Foundation for Healthcare Improvement (CFHI) has supported healthcare organizations across Canada to meaningfully partner with patients in quality improvement and system redesign efforts. This article describes CFHI initiatives to enhance patient engagement efforts across Canada and the lessons learned in the context of "engagement-capable environments" and offers reflections for the future of patient engagement in Canada.
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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 | 0.044 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.054 | 0.028 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.006 | 0.029 |
| Research integrity | 0.009 | 0.021 |
| Insufficient payload (model declined to judge) | 0.007 | 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".