Application of continuous quality improvement to medical education
Bibliographic record
Abstract
CONTEXT: The explicit, intentional and systematic application of continuous quality improvement (QI) in medical education practice and research can improve medical education and help it achieve its goals. Quality improvement and medical education share a foundation centred on learning-experiencing, reflecting, thinking and acting in continuous cycles that spiral to sustained advancement. This suggests that a QI mindset can be brought to bear on various aspects of medical education research and practice. DISCUSSION: To explore this possibility, we turn to W. Edwards Deming's System of Profound Knowledge, widely regarded as one of the foundational frameworks in quality improvement, where he argues strongly that there are four highly interrelated elements that are required for improvement: Appreciation of a System, Theory of Knowledge, Knowledge about Variation and Knowledge of Psychology. In this article, we define and explore each of the four domains and their application in medical education, highlighting both opportunities and challenges. CONCLUSION: Medical educators who utilise QI in their educational practices can help create learning environments that imprint positively on learners and contribute to better outcomes in their clinical learning environments. We provide recommendations for how educators' informed use of QI can improve medical education and help it achieve its ultimate goal of improved health and health care.
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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 | 0.024 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".