Building the Bridge to Quality: An Urgent Call to Integrate Quality Improvement and Patient Safety Education With Clinical Care
Bibliographic record
Abstract
Current models of quality improvement and patient safety (QIPS) education are not fully integrated with clinical care delivery, representing a major impediment toward achieving widespread QIPS competency among health professions learners and practitioners. The Royal College of Physicians and Surgeons of Canada organized a 2-day consensus conference in Niagara Falls, Ontario, Canada, called Building the Bridge to Quality, in September 2016. Its goal was to convene an international group of educational and health system leaders, educators, frontline clinicians, learners, and patients to engage in a consensus-building process and generate a list of actionable strategies that individuals and organizations can use to better integrate QIPS education with clinical care.Four strategic directions emerged: prioritize the integration of QIPS education and clinical care, build structures and implement processes to integrate QIPS education and clinical care, build capacity for QIPS education at multiple levels, and align educational and patient outcomes to improve quality and patient safety. Individuals and organizations can refer to the specific tactics associated with the 4 strategic directions to create a road map of targeted actions most relevant to their organizational starting point.To achieve widespread change, collaborative efforts and alignment of intrinsic and extrinsic motivators are needed on an international scale to shift the culture of educational and clinical environments and build bridges that connect training programs and clinical environments, align educational and health system priorities, and improve both learning and care, with the ultimate goal of achieving improved outcomes and experiences for patients, their families, and communities.
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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 | 0.133 | 0.163 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.017 | 0.033 |
| Scholarly communication | 0.037 | 0.047 |
| Open science | 0.012 | 0.037 |
| Research integrity | 0.052 | 0.077 |
| Insufficient payload (model declined to judge) | 0.027 | 0.006 |
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".