Quality Improvement in Medical Education: Implications for Curriculum Change
Bibliographic record
Abstract
To the Editor: Logically, it makes sense to begin training future physicians in quality improvement (QI) during their undergraduate and postgraduate studies. It is understandable, however, that finding a time and place to teach QI during these years can be challenging. Some might argue that teaching QI is not necessarily a priority, or cannot be done in a setting where there is already so little space and time to get the “core content” taught. I propose that the teaching of QI be integrated throughout medical training. QI should certainly be introduced as part of the core content, but it need not be isolated from the remainder of the medical curriculum. Information and knowledge that is taught should be constantly reinforced through various opportunities and courses. For example, an institution could begin with teaching the core principles of QI in the first year of medical school, and then allow students to think of a potential QI project they are interested in doing. Students can be motivated to choose a topic that is related to what they are learning at the time. For example, if the current core content focuses on cardiac diseases and management, students can explore QI initiatives that increase screening for dyslipidemia, educate patients on risk factors, etc. By integrating the QI education into the existing curriculum, medical schools can help students realize the importance of applying critical-thinking and problem-solving skills in the field of health care; this promotes their development as medical experts who are prepared for future practice. As a current medical student, I strongly believe in the power of QI as a means for physicians to improve the health care system and support their patients. Being educated about QI in the undergraduate medical curriculum would allow me and my fellow students to be exposed to opportunities in which we are able to see the applicability of QI not only as future physicians but also as current medical students. Placing the QI curriculum in context with the rest of the medical core content to be taught would make this more relevant and would allow students to integrate their learning from various contexts. Early exposure and training in QI in medical school would also allow students to develop a passion for QI and to understand how it can be applied in the future. I strongly advocate for universities to consider these approaches to teaching QI during the undergraduate (and postgraduate) years, as the benefits to learners are powerful—both currently as medical students, and in the future as health care providers. MarinaAbdel MalakSecond-year medical student, University of Toronto Faculty of Medicine, Toronto, Ontario, Canada; [email protected]
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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 | 0.032 | 0.200 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.021 | 0.028 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".