Exploring a Co-Designed Approach for Healthcare Quality Improvement—Learning Through Developmental Evaluation
Bibliographic record
Abstract
BACKGROUND: Healthcare setting teams were challenged to understand how and what to measure regarding healthcare quality improvement (HQI), who should be involved, and what approach to apply. We aimed to determine if a generic co-design approach involving patients/families, multi-disciplinary care providers, and other staff was feasible to apply for HQI across diverse care settings. Developmental evaluation embedded in the co-design approach would determine its effectiveness, challenges, and other experiences across care settings and teams. METHODS: Twenty-two acute and community care settings agreed to participate in applying a phased co-design approach to their HQI initiatives, including developmental evaluation. Each care setting team received co-design orientation and support. Semi-structured interviews and focus groups were conducted with patient/family advisors (PFAs) and care setting staff/care providers to gather their experiences with the co-design approach applied to their phased HQI work. Transcripts were thematically analyzed and triangulated with observation notes of care setting team discussions. Experiences were gathered from 17 PFAs and 68 staff/care providers across the 22 participating healthcare settings. RESULTS: Themes for the orientation and each phase emphasized the importance of participants' understanding, engagement, and ongoing open communication throughout the HQI co-design process. The orientation was viewed as key to facilitating good outcomes. Participants valued working together, gathering real-time experiences to "make a difference", and having PFA voices involved in co-designing the HQI initiatives. Challenges were identified, including time commitment. CONCLUSIONS: Based on the overall developmental evaluation findings, there was consensus that a generic co-design of HQI initiatives was effective, feasible, and sustainable across care settings.
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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 | 0.012 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".