When doctors go to business school: career shoices of physician-MBAs.
Bibliographic record
Abstract
There has been substantial growth in the number of physicians pursing Master of Business Administration (MBA) degrees over the past decade, but there is continuing debate over the utility of these programs and the career outcomes of their graduates. The authors analyzed the clinical and professional activities of a large cohort of physician-MBAs by gathering information on 206 physician graduates from the Harvard Business School MBA program who obtained their degrees between 1941 and 2014. Key outcome measures that were examined include medical specialty, current professional activity, and clinical practice. Chi square tests were used to assess the correlations in the data. Among the careers that were tracked (n = 195), there was significant heterogeneity in current primary employment. The most common sectors were clinical (27.7%), investment banking/finance (27.0%), hospital/provider administration (11.7%), biotech/device/pharmaceutical (10.9%), and entrepreneurship (9.5%). Overall, 84% of physician-MBAs entered residency; approximately half (49.3%) remained clinically active in some capacity and only one-fourth (27.7%) reported clinical medicine as their primary professional role. Among those who pursued residency training, the most common specialties were internal medicine (39.3%), emergency medicine (10.4%), orthopedic surgery (9.2%), and general surgery (8.6%). Physician-MBAs trained in internal medicine were significantly more likely to remain clinically active (63.8% vs 42.4%; P = .01). Clinical activity and primary employment in a clinical role decreased after degree conferment. After completing their education, a majority of physician-MBAs divert their primary professional focus away from clinical activity. These findings reveal new insights into the career outcomes of physician-MBAs.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".