Community-engaged co-design of a quality improvement capacity building program within an integrated health system in Ontario, Canada
Bibliographic record
Abstract
Purpose An integrated care system identified quality improvement (QI) capacity as a gap in advancing their integrated quality care priorities and improvement efforts. Here we describe the design and implementation of a QI capacity building program that aimed to (1) build QI capacity amongst diverse integrated care system members and (2) apply QI principles to advance integrated quality care priorities. Design/methodology/approach The integrated care system leaders, including community members, partnered with the University of Toronto Centre for Quality Improvement and Patient Safety to co-design and deliver the QI capacity building program focused on improving cancer screening rates. An existing acute care capacity building program was adapted. Content included QI tools, data to identify and monitor QI priorities, equity considerations, and empowering participants as change agents. Findings Participants were satisfied with the content and delivery of the program. Some described using QI tools and strategies in practice following the workshop. Challenges to using the tools included the current pressures facing primary care and the health system, resources, and data availability. Practical implications This QI capacity building program was challenging but feasible. Clarifying the target audience, being attentive to co-design, acknowledging post-pandemic system challenges and proactively addressing variable knowledge and barriers to QI work in practice will inform future iterations of this program. Originality/value While many examples of QI education programs exist, the majority target a single healthcare sector. We describe a novel QI capacity building model that bridges healthcare sectors and includes patient partners and community members as teachers and participants.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".