A Constructive Reframing of Student Roles and Systems Learning in Medical Education Using a Communities of Practice Lens
Bibliographic record
Abstract
Health systems are in the midst of a transformation that is being driven by a variety of forces. This has important implications for medical educators because clinical practice environments play a key role in learning and professional development, and evolving health systems are beginning to demand that providers have "systems-ready" knowledge, attitudes, and skills. Such implications provide a clear mandate for medical schools to modify their goals and prepare physicians to practice flexibly within teams and effectively contribute to the improvement of health care delivery. In this context, the concepts of value-added medical education, authentic student roles, and health systems science are emerging as increasingly important. In this Article, the authors use a lens informed by communities of practice theory to explore these three concepts, examining the implications that the communities of practice theory has in the constructive reframing of educational practices-particularly common student roles and experiences-and charting future directions for medical education that better align with the needs of the health care system. The authors apply several key features of the communities of practice theory to current experiential roles for students, then propose a new approach to students' clinical experiences-value-added clinical systems learning roles-that provides students with opportunities to make meaningful contributions to patient care while learning health systems science at the patient and population level. Finally, the authors discuss implications for professional role formation and anticipated challenges to the design and implementation of value-added clinical systems learning roles.
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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 | 0.022 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.068 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.006 | 0.011 |
| 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".