Two sides of the same coin: Quality improvement and program evaluation in health professions education
Bibliographic record
Abstract
Health professions education is in constant pursuit of new ways of teaching and assessment in order to improve the training of healthcare professionals. Educators are often challenged with designing, implementing, and evaluating programs in the context of their professional practice, particularly those in response to dynamic and emerging social needs. This article explores the synergies and intersections of two approaches-quality improvement and program evaluation-and the potential utility of their combinations within our field to design, evaluate, and most importantly, improve educational programming. We argue that the inclusion of established quality improvement frameworks within program evaluation provides a proven mechanism for driving change, can optimize programming within the multi-contextual education systems, and, ultimately, that these two approaches are complementary to one another. These combinations hold great promise for optimizing programming in alignment with social missions, where it has been difficult for institutions worldwide to generate and capture evidence of social accountability.
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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 | 0.347 | 0.435 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.007 | 0.100 |
| Scholarly communication | 0.030 | 0.043 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.023 | 0.023 |
| Insufficient payload (model declined to judge) | 0.005 | 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".