17. Visiting Student Diversity Scholarships: A Cross-sectional Analysis of Plastic Surgery Residency Programs
Bibliographic record
Abstract
Background: The average number of completed away rotations plastic surgery applicants is 2 to 3 [1]. A recent study found the average cost of one away rotation to be $2,000 [2]. These costs do not include auxiliary costs of the residency application process. These financial constraints bar many students from the ability to complete the number of rotations needed to be a competitive applicant. Many programs have developed monetary scholarships and support for students falling in the category of underrepresented in medicine. Our team sought to provide a centralized resource for URM students seeking away rotations with funded opportunities. Methods: We conducted a cross sectional analysis of URM scholarships opportunities offered by ERAS-participating integrated plastic surgery programs (n=84) between April and July 2022. The study utilized residency and program web domains. Investigators collected information on the monetary value of scholarship, eligibility criteria, required application documents, and if interviews were guaranteed. Results: The study found that approximately 42.8% (n=36) of programs have in place monetary support for URM visiting medical students (e.g. stipend or reimbursement). The average value of stipends was $1985 and of reimbursement was $2500. Most programs defined eligibility of URM status in a statement similar to that of the AAMC’s. Low socioeconomic status was accounted for in eight programs. Students without home programs were included in as subset of eligible applicants in only 1 opportunity. Programs for the most part catered only to 4th year medical students and required USMLE Step 1 scores and Step 2 scores when available. Number of LOR required ranged from one to three, and the number of essays averaged at 1.4 (range: 1 to 6). Only two programs explicitly guaranteed an interview for residency. Conclusion: This study provides a centralized resource for URM students interested in completing aways. Further investigation into how residency programs developed and funded these scholarship opportunities would be of much benefit. These insights could help guide other programs in the development of their own visiting medical student scholarships for URM applicants.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".