Bibliographic record
Abstract
The authors articulate the importance of integrating stewardship within the larger medical education curriculum in order to fully realize the goals of the Choosing Wisely campaign. When the ABIM Foundation, Consumer Reports, and nine medical specialty societies launched Choosing Wisely in 2012, we initially envisioned it as an effort to address overuse and waste in the U.S. health care system. We have been inspired to see the campaign and its ideals embraced internationally by our colleagues in Canada, as well as in countries including Australia, the Netherlands, and the United Kingdom. While the goal of Choosing Wisely is simple—to encourage clinicians and patients to engage in conversations about avoiding unnecessary care—achieving it is far more complex. As Leon-Carlyle and colleagues rightly posit, teaching stewardship competencies as part of medical education is a crucial step in ensuring clinicians develop critical thinking skills and are prepared to practice evidence-based medicine when they get to the bedside. To support clinicians in this endeavor, the ABIM Foundation has undertaken several initiatives aimed at catalyzing development of new approaches in medical education and training. In partnership with Costs of Care, the ABIM Foundation launched the Teaching Value in Health Care Learning Network1 to give early adopters and innovators in medical education a venue to share best practices and learnings. The community seeks to inspire others to adapt these ideas and implement them in their own institutions, and a monthly webinar series highlights implementation models and innovations in value-based training. The foundation has also partnered with Costs of Care to run the Teaching Value and Choosing Wisely Challenge,2 which aims to identify promising innovations and bright ideas for teaching high-value care and stewardship to medical students, trainees, and faculty. Over the past two years more than 150 entries have been submitted and a dozen winners declared. Most recently, the ABIM Foundation funded several projects3 that will foster innovations and new approaches to integrating stewardship competencies and better decision making in medical education and training. Much work is still needed until, as the authors write, resource stewardship becomes a “norm in medical practice.” I am encouraged by the growing momentum being generated by the ABIM Foundation’s programs, as well as the work of the authors and many others, to address these challenges and help prepare future clinicians to provide the best care possible for patients. Daniel B. Wolfson, MHSA Executive vice president and chief operating officer, ABIM Foundation, Philadelphia, Pennsylvania; [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 | 0.009 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.046 | 0.074 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".