Dual Autonomies, Divergent Approaches: How Stratification in Medical Education Shapes Approaches to Patient Care
Bibliographic record
Abstract
The United States relies on international and osteopathic medical graduates ("non-USMDs") to fill one third of residency positions because of a shortage of American MD graduates ("USMDs"). Non-USMDs are often informally excluded from top residency positions, while USMDs tend to fill the most prestigious residencies. Little is known, however, about whether the training in these different settings is comparable or how it impacts patients. Drawing on 23 months of ethnographic fieldwork and 123 interviews, I compare training at two internal medicine programs: a community hospital staffing 90% non-USMDs and a university hospital staffing 99% USMDs. The community program's structure lent itself to a hands-off approach resulting in "inconsistent autonomy." In contrast, the university hospital supervised its residents much more regularly, resulting in "supported autonomy." I conclude that medicine may be stratified in unexpected ways between USMDs and non-USMDs and that stratification may matter for patients.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.028 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".