Patient and Staff Engagement in Health System Improvement: A Qualitative Evaluation of the Experience-Based Co-design Approach in Canada
Bibliographic record
Abstract
Surveys and interviews were undertaken in Ontario, Canada, with healthcare staff, patients, caregivers and family members to evaluate the adoption and effectiveness of the experience-based co-design (EBCD) approach. EBCD combines patient and staff experiences to identify opportunities for healthcare improvement. Participants reported that EBCD was an effective form of improving experience. Implementation barriers included time, human resources and funding. Suggestions for increased EBCD utilization included funding, training, promotion of success stories, leadership and greater participant involvement. EBCD can be an effective method of identifying and transforming how healthcare services are delivered to improve the patient, caregiver and family experience.
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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 | 0.057 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.012 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".