Learners as Leaders: A Global Groundswell of Students Leading Choosing Wisely Initiatives in Medical Education
Bibliographic record
Abstract
Resource stewardship and reducing low-value care have emerged as urgent priorities for health care delivery systems worldwide. However, few medical schools' curricula include adequate content to allow learners to master the knowledge, skills, and attitudes needed to contribute to this transformation toward value-based health care. This article describes a program to launch student-led curriculum enhancement initiatives in 7 countries. The program, called STARS (Students and Trainees Advocating for Resource Stewardship), was inspired by Choosing Wisely, a campaign by the American Board of Internal Medicine Foundation that seeks to promote conversations on avoiding unnecessary medical tests, treatments, and procedures.The initial STARS model, which originated in Canada in 2015, included a leadership summit, where students from multiple medical schools learned about Choosing Wisely principles, leadership, and advocacy. These students then led grassroots efforts at their local medical schools with faculty and other students to raise awareness and advocate for changes related to resource stewardship. Student-led efforts resulted in the integration of Choosing Wisely principles into case-based learning, the creation of student interest groups and electives, the launch of social media campaigns, and the organization of special presentations by local experts.The rapid spread of similar programs in 6 other countries (Italy, Japan, the Netherlands, New Zealand, Norway, and the United States) by 2018 suggests that STARS resonates across multiple settings and signals the potential for such a model to advance other important areas in medical education. This article documents results and lessons learned from the first 4 years of the program.
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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 | 0.021 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.018 |
| Scholarly communication | 0.024 | 0.021 |
| Open science | 0.003 | 0.036 |
| Research integrity | 0.010 | 0.019 |
| 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".