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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".