Improving Transparency in the Residency Application Process: Survey Study
Bibliographic record
Abstract
BACKGROUND: Increasing numbers of residency applications create challenges for applicants and residency programs to assess if they are a good fit during the residency application and match process. Applicants face limited or conflicting information as they assess programs, leading to overapplying. A holistic review of residency applications is considered a gold standard for programs, but the current volumes and associated time constraints leave programs relying on numerical filters, which do not predict success in residency. Applicants could benefit from increased transparency in the residency application process. OBJECTIVE: This study aims to determine the information applicants find most beneficial from residency programs when deciding where to apply, by type of medical school education background. METHODS: Match 2023 applicants voluntarily completed an anonymous survey through the Twitter and Instagram social media platforms. We asked the respondents to select 3 top factors from a multiple-choice list of what information they would like from residency programs to help determine if the characteristics of their application align with program values. We examined differences in helpful factors selected by medical school backgrounds using ANOVA. RESULTS: There were 4649 survey respondents. When responses were analyzed by United States-allopathic (US-MD), doctor of osteopathic medicine (DO), and international medical graduate (IMG) educational backgrounds, respondents chose different factors as most helpful: minimum United States Medical Licensing Examination (USMLE) or Comprehensive Osteopathic Medical Licensing Examination (COMLEX) Step 2 scores (565/3042, 18.57% US-MD; 485/3042, 15.9% DO; and 1992/3042, 65.48% IMG; P<.001), resident hometown region (281/1132, 24.82% US-MD; 189/1132, 16.7% DO; and 662/1132, 58.48% IMG; P=.02), resident medical school region (476/2179, 22% US-MD; 250/2179, 11.5% DO; and 1453/2179, 66.7% IMG; P=.002), and percent of residents or attendings underrepresented in medicine (417/1815, 22.98% US-MD; 158/1815, 8.71% DO; and 1240/1815, 68.32% IMG; P<.001). CONCLUSIONS: When applying to residency programs, this study found that the factors that respondents consider most helpful from programs in deciding where to apply differ by educational background. Across all educational groups, respondents want transparency around standardized exam scores, geography, and the racial or ethnic backgrounds of residents and attendings.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".