Can first-year medical students acquire quality improvement knowledge prior to substantial clinical exposure? A mixed-methods evaluation of a pre-clerkship curriculum that uses education as the context for learning
Bibliographic record
Abstract
BACKGROUND: Quality Improvement (QI) training for health professionals is essential to strengthen health systems. However, QI training during medical school is constrained by students' lack of contextual understanding of the health system and an already saturated medical curriculum. The Program for Improvement in Medical Education (PRIME), an extracurricular offered at the Michael G. DeGroote School of Medicineat McMaster University (Hamilton, Canada), addresses these obstacles by having first-year medical students engage in QI by identifying opportunities for improvement within their own education. METHODS: A sequential explanatory mixed-methods approach, which combines insights derived from quantitative instruments and qualitative interview methods, was used to examine the impact of PRIME on first-year medical students and the use of QI in the context of education. RESULTS: The study reveals that participation in PRIME increases both knowledge of, and comfort with, fundamental QI concepts, even when applied to clinical scenarios. Participants felt that education provided a meaningful context to learn QI at this stage of their training, and were motivated to participate in future QI projects to drive real-world improvements in the health system. CONCLUSIONS: Early exposure to QI principles that uses medical education as the context may be an effective intervention to foster QI competencies at an early stage and ultimately promote engagement in clinical QI. Moreover, PRIME also provides a mechanism to drive improvements in medical education. Future research is warranted to better understand the impact of education as a context for later engagement in clinical QI applications as well as the potential for QI methods to be translated directly into education.
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How this classification was reachedexpand
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 | 0.090 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".