Compassionate Off-Ramps: The Availability of Terminal Master's Degrees in US Medical Schools
Bibliographic record
Abstract
Medical students who underperform or find they are not a "good fit" for medicine have limited options. A terminal master's degree represents an exit alternative that recognizes students' completed coursework and acknowledges their commitment to the medical sciences. Although medical educators have called for the creation of such programs, termed "compassionate off-ramps," the prevalence of degree offerings in US programs is unknown. In the fall of 2020, a survey was sent to Student Affairs Deans at 141 LCME-accredited MD programs; 73 institutions responded (52%). Terminal master's degrees were offered by 19% of respondent institutions (n = 13). While 85% of those without a terminal master's (n = 48) endorsed degree benefits, only 36% (n = 21) had plans to create the degree. This study demonstrates that few US medical schools offer a terminal master's degree, leaving students who exit medicine with high levels of debt without an avenue for a degree to support employment or future academic pursuits. The authors identify implications for students, particularly those who are at a higher risk of failing Step 1, such as students who are underrepresented in medicine, socioeconomically disadvantaged, or who have a disability and are unaccommodated. Potential barriers to terminal master's program creation are identified and mitigating strategies are recommended.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".