Teaching Quality Improvement in Graduate Medical Education
Bibliographic record
Abstract
PROBLEM: An emerging priority in medical education is the need to facilitate learners' acquisition of quality improvement (QI) competencies. Accreditation bodies in both Canada and the United States have included QI and patient safety in their core competencies. APPROACH: In 2010, the Department of Family Medicine at Queen's University designed a graduate medical education curriculum to engage residents in a clinical QI program that would meet accreditation requirements. Monthly didactic sessions were combined with an experiential, team-based QI project that aligned with existing clinic priorities. The curriculum spans the first year of residency and is divided into three stages: (1) Engaging, (2) Understanding, and (3) Improving and translating. In Stage 1, teams of residents select a clinical QI topic, engage stakeholders, and collect baseline data related to their topic. In Stage 2, they focus on understanding their problem, interpreting their results, and applying QI tools. In Stage 3, they develop change ideas, translate their knowledge, and prepare to hand over their project. OUTCOMES: This QI curriculum aided residents in effectively acquiring QI competencies and allowed them to experience real-world challenges, such as securing project buy-in, negotiating with peers, and developing solutions to problems. Unlike in many QI programs, residents learned how to improve quality rather than about QI; thus, they formed the necessary foundation to carry out QI work in the future. NEXT STEPS: The curriculum will be evaluated using a knowledge assessment and satisfaction tool and postproject resident interviews. Facilitators will focus more on improving faculty develop ment in QI.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".