Applying improvement science to establish a resident sustained quality improvement (QI) educational model
Bibliographic record
Abstract
BACKGROUND: Prior to 2017, internal medicine (IM) residents at the University of Alberta did not have a standardised quality improvement (QI) educational curriculum. Our goal was to use QI principles to develop a resident sustained curriculum using the Evidence-based Practice for Improving Quality (EPIQ) training course. METHODS: Three one-year Plan-Do-Study-Act (PDSA) cycles were conducted. The EPIQ course was delivered to postgraduate year (PGY) 1-3 residents (n=110, PDSA 1) in 2017, PGY-1 residents (n=27, PDSA 2) in 2018 and PGY-1 residents (n=28, PDSA 3) in 2019. Trained residents were recruited as facilitators for PDSA 2 and 3. Residents worked through potential QI projects that were later presented for evaluation. Precourse and postcourse surveys and tests were conducted to assess knowledge acquisition and curriculum satisfaction. Process, outcome and balancing measures were also evaluated. RESULTS: In PDSA 1, 98% felt they had acquired understanding of QI principles (56% increase), 94% of PGY-2 and PGY-3 residents preferred this QI curriculum compared with previous training, and 65% of residents expressed interest in pursuing a QI project (15% increase). In PDSA 2, tests scores of QI principles improved from 77.6% to 80%, and 40% of residents expressed interest in becoming a course facilitator. In PDSA 3, self-rated confidence with QI methodology improved from 53% to 75%. A total of 165 residents completed EPIQ training and 11 residents became course facilitators. CONCLUSIONS: Having a structured QI curriculum and working through practical QI projects provided valuable QI training for residents. Feedback was positive, and with each PDSA cycle there was increased resident interest in QI. Developing this curriculum using validated QI tools highlighted areas of change opportunity thereby enhancing acceptance. As more cycles of EPIQ are delivered and more residents become facilitators, it is our aim to have this curriculum sustained by future residents.
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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 | 0.025 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".