Social Science and Humanities Research in MD–PhD Training
Bibliographic record
Abstract
To the Editor: As three of the few MD–PhD students completing PhDs in social science and humanities disciplines, we would like to add our voices to those of Ryan O’Mara and colleagues1 in calling on MD–PhD programs to train graduates in disciplines beyond the biomedical and clinical sciences. The authors convincingly argue that clinician–researchers trained in diverse disciplines such as public policy, management, and social sciences can promote health and health equity specifically through addressing their social determinants. To improve health and health equity for our patients, we must also train physicians in the knowledge and expertise to properly achieve these ends. We would like to highlight two ways that social science and humanities research in MD–PhD training can contribute. First, as a research community we must answer the question, what are the components of physician knowledge and expertise? The components surely include clinical reasoning, clinical judgment, narrative competency, empathy, professional and ethical behavior, leadership, and advocacy. Many of these elements are considered part of the ineffable art of medicine, elusive and resistant to analysis. Yet they are in fact researchable by social scientists and humanities scholars.2 Psychologists, bioethicists, and philosophers of medicine study clinical reasoning and judgment; English and narrative medicine scholars study narrative competency and empathy; and finally, historians of medicine, medical anthropologists, and medial sociologists study professional, cultural, and ethical norms, the politics of practice, and the origins of health inequalities. Second, educators need research on how to best train physicians in this knowledge and skill set, including how it is acquired in medical education, how the culture of training influences its uptake, and the effectiveness of various strategies to teach and assess it. Here again these questions can be answered through a social science lens, using theory and methodologies—experimental, observational, quantitative, qualitative—from various disciplines. As medical students and future physicians, MD–PhD trainees have a unique perspective from which to undertake these two types of research and are well positioned to become future leaders in education who will help train a medical community in the diverse knowledge and expertise needed to promote good health for all. Jonathan Fuller MD–PhD student, University of Toronto, Toronto, Ontario, Canada; [email protected] Tavis Apramian, MA, MSc MD–PhD student, Western University, London, Ontario, Canada. Cynthia Min MD–PhD student, University of British Columbia, Vancouver, British Columbia, Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.053 | 0.259 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.033 | 0.041 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".