Faculty Development in Improvement Science: Building Capacity and Expanding Curricula Across an Academic Health Center
Bibliographic record
Abstract
ABSTRACT Background The ability of health professions faculty to design, teach, evaluate, and improve relevant curricula is vital for teaching improvement science (IS) skills to trainees. Objective We launched a Foundational Improvement Science Curriculum (FISC) to build faculty competence in IS teaching and scholarship, and to develop, expand, and standardize IS curricula across one institution. Methods FISC consisted of 9 full or half-day sessions over 10 months in 2015–2016 and 2016–2017 academic years. Each session required pre-work, including readings, Institute for Healthcare Improvement Open School modules, and personal improvement projects. Sessions included brief didactics, group activities, planning, and feedback on curriculum development. An evaluation strategy was employed, including pre- and post-program self-assessment, competency mapping, evaluations of didactics and overall program, and participant satisfaction. Results Forty individuals from 23 academic programs voluntarily completed FISC, representing 20% of graduate medical education (GME) programs and 50% of primary GME programs in addition to undergraduate medical education (UME) and nursing programs. Median self-assessed competency scores (mid versus final score; scale 1–9, 9 high; P < .05 for all comparisons) improved over the course for all competencies for knowledge (3 versus 7), application (2 versus 7), curriculum design (2 versus 7), and scholarship (2 versus 5). Eighteen new or revised IS curricula were developed across GME, UME, and nursing programs. Conclusions FISC offers a feasible model to enhance and support faculty development in IS and IS curriculum design.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".