Building Capacity for Quality: A Pilot Co-Learning Curriculum in Quality Improvement for Faculty and Resident Learners
Bibliographic record
Abstract
BACKGROUND: Despite a mandate to teach quality improvement (QI) to residents, many training programs lack faculty capacity to deliver a QI curriculum. OBJECTIVE: We piloted a co-learning curriculum in QI to train residents while simultaneously developing QI teachers. We evaluated the curriculum's acceptability and feasibility and its effect on faculty engagement in doing and teaching QI. METHODS: The curriculum involved 2 half-day, interactive sessions, a team-based QI project, and end-of-year project presentations. Key curriculum design principles included (1) residents and faculty co-attend all interactive sessions, (2) residents and faculty work together on team-based QI projects, and (3) QI projects align with divisional QI priorities. Using the Kirkpatrick framework for learner outcomes, we focused our program evaluation on Level 1 (satisfaction) and Level 2 (knowledge and skills acquisition) outcomes using year-end curriculum evaluations. RESULTS: Our study included 14 residents (70%) and 6 faculty members (30%). With respect to satisfaction (Kirkpatrick Level 1 outcome), 93% (13 of 14) of residents and 100% (6 of 6) of faculty participants rated the overall curriculum as "above average" or "outstanding." Regarding faculty knowledge and skills acquisition (Kirkpatrick Level 2 outcomes), faculty self-rated their QI knowledge and interest in QI higher than their intent to incorporate QI into future practice and their comfort in teaching or supervising QI projects. All 5 faculty respondents (100%) rated the co-learning model for faculty development in QI as "above average" or "outstanding." CONCLUSIONS: Teaching QI to residents and faculty as co-learners is feasible and acceptable and offers a promising model for programs to teach QI to residents while concurrently building faculty capacity.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".