Co-designing Healthcare Quality Improvement: The Kovacs Burns & George Orientation Guide
Bibliographic record
Abstract
To prepare healthcare organizations and patients/families to be equally ready to become partners in co-designing healthcare policy, practices, and improvements, there is a need to (1) understand how "co-design ready" organizations and their staff and care providers are to co-design health care policies, practices, and improvements with patients and families; (2) understand how prepared patients and families, as users of the health system, are to step into co-designer roles with confidence so that their voices will be heard as they influence the development or changes to improve healthcare system policies, services, practices, and products; (3) anticipate and/or address challenges with meeting the expectations of what is involved with the co-design approach, including with recruiting, preparing, and training care setting leaders, staff/care providers, and patient/family advisors; (4) ensure care settings provided appropriate tools and resources to support co-design; and (5) guide the shift in culture from engagement to co-design. Recommendations for enabling co-design in care settings include providing an orientation and preparation workshop and guide/workbook. An example of an orientation and preparation workshop is shared.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.027 | 0.018 |
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".