Bibliographic record
Abstract
We thank Drs. Barwise and Liebow for their response to our Perspective. We agree that medical students are not wholly responsible for changing how the social determinants of health (SDOH) are taught. This is primarily an institutional responsibility—medical schools, academic health science centers, and hospital leadership must ensure that future physicians learn that medicine includes upstream action on SDOH at individual, practice, and community levels.1 The authors note that most students “will become clinicians—not public policy experts or social scientists.” Although ostensibly true, this fails to recognize that physician voices are incredibly influential in public policy discussions. Clinicians are limited at best and ineffectual at worst when they are unequipped to screen for and address the impact of SDOH on their patients’ lives. We are not arguing that faculty must inculcate all medical students into a particular worldview. We are, however, arguing that they should prompt students to question why and how issues, such as racism and poverty, result in deleterious health effects and consider how these conditions can be changed. Drs. Barwise and Liebow state that it is unrealistic to expect medical school curricula to change to incorporate our proposals. We argue that approaches in medical education have changed dramatically in the past and can do so again. Decades ago, the notion of teaching humanism through storytelling and poetry in medical schools would have likely seemed impossible, and yet it is now routinely done in medical schools globally.2,3 Lastly, the authors state that “inequality will not concern everyone equally.” We find this problematic and respectfully propose that it should. We are tasked with caring for all members of society, and especially the most marginalized for whom health and well-being are particularly at risk. Physicians are uniquely poised to bear witness to the downstream effects of social disparities. They are trusted members of society who are often in positions of leadership and authority. This trust is eroded when we trade in band-aids without calling for upstream solutions. Malika Sharma, MD, MEdMedical director, Casey House and HIV Physician, Maple Leaf Medical Clinic Toronto, Ontario, Canada; [email protected]Andrew D. Pinto, MD, MScAssistant professor, Department of Family and Community Medicine, Faculty of Medicine and Dalla Lana School of Public Health, University of Toronto, and clinician–scientist, MAP/Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, Ontario, Canada.Arno K. Kumagai, MDProfessor and vice-chair of education, Department of Medicine, University of Toronto, and F.M. Hill Chair for Humanism Education, Women’s College Hospital, Toronto, Ontario, Canada.
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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 | 0.013 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.050 | 0.102 |
| Insufficient payload (model declined to judge) | 0.010 | 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".