Beyond the M.D.: Transdisciplinary approaches of high-volume dual degree M.D./Masters programs at U.S. allopathic medical schools
Bibliographic record
Abstract
PURPOSE: Transdisciplinarity has been described as a fusion of theories, methods, and expertise across disciplinary boundaries to address complex, global problems. This approach has coincided with an increase in US medical schools offering masters degrees along with an MD degree to equip medical students to practice in complex, interconnected health systems. This study focused on medical schools that graduate the most dual degree students per year and explored the alignment of such programs with a transdisciplinary approach. METHODS: We identified 19 allopathic medical schools that annually graduated an average of 10 or more dual-degree students from 2015-2020. We surveyed these schools and asked participants to describe the reason(s) their institutions offered dual-degree programs. Two authors coded the narrative responses from the survey. RESULTS: Responses were received from 17 of the 19 schools. The analysis of participants' responses regarding their institutions' purpose for offering dual programs revealed several themes associated with a transdisciplinary approach to training. The most common themes were expand skill sets beyond a medical degree (73%), provide opportunity for interdisciplinary collaboration (67%), expand career interest and goals (60%), develop leaders (53%), enhance residency applications (47%) and further the institution's vision and mission (45%). CONCLUSIONS: This study is the first comprehensive evaluation of MD/Masters programs in the United States that includes a summary of the medical schools with the largest dual degree programs and their reasons for offering them. The findings support the hypothesis that allopathic medical schools recognize the need for a transdisciplinary approach to prepare students for the complexities in healthcare. These programs provide students with opportunities for additional areas of expertise, leadership development, enhancement of competitiveness for residency application, and interdisciplinary collaboration. Medical schools without dual-degree programs may consider developing these programs to provide benefits to students and institutions.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".