Faculty–Resident “Co-learning”: A Longitudinal Exploration of an Innovative Model for Faculty Development in Quality Improvement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: To examine the effectiveness of co-learning, wherein faculty and trainees learn together, as a novel approach for building quality improvement (QI) faculty capacity. METHOD: From July 2012 through September 2015, the authors conducted 30 semistructured interviews with 23 faculty participants from the Co-Learning QI Curriculum of the Department of Medicine, Faculty of Medicine, University of Toronto, and collected descriptive data on faculty participation and resident evaluations of teaching effectiveness. Interviewees were from 13 subspecialty residency programs at their institution. RESULTS: Of the 56 faculty participants, the Co-Learning QI Curriculum trained 29 faculty mentors, 14 of whom taught formally. Faculty leads with an academic QI role, many of whom had prior QI training, reinforced their QI knowledge while also developing QI mentorship and teaching skills. Co-learning elements that contributed to QI teaching skills development included seeing first how the QI content is taught, learning through project mentorship, building experience longitudinally over time, a graded transition toward independent teaching, and a supportive program lead. Faculty with limited QI experience reported improved QI knowledge, skills, and project facilitation but were ambivalent about assuming a teacher role. Unplanned outcomes for both groups included QI teaching outside of the curriculum, applying QI principles to other work, networking, and strengthening one's QI professional role. CONCLUSIONS: The Co-Learning QI Curriculum was effective in improving faculty QI knowledge and skills and increased faculty capacity to teach and mentor QI. Findings suggest that a combination of curriculum and contextual factors were critical to realizing the curriculum's full potential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it