Physical Attractiveness and Chances of Being Invited to Interview With a Medical Residency Program: Retrospective Cohort Study
Bibliographic record
Abstract
Abstract Background Applicants participating in the Residency Match generally submit a photograph through the Electronic Residency Application Service (ERAS). Studies demonstrate that subjectively more attractive applicants are more likely to succeed during job recruitment, including a paper related to the Residency Match. Objective This study further investigates the relationship between an applicant’s attractiveness and the likelihood that they are invited to interview with a residency program to explore if more attractive applicants are more likely to be invited to interview when controlled for demographic and academic variables. If there are enough data suggesting that an ERAS photograph being visible prior to the interview gives an unfair advantage to more attractive applicants, this practice might be reconsidered by some residency programs or by ERAS itself. Methods Residency directors were surveyed on application review practices. Programs that viewed ERAS photographs prior to deciding whether to invite an applicant to interview were asked to share ERAS files of all reviewed applicants of the 2022 Match. A machine learning model was used to determine attractiveness scores for ERAS photographs. The scores ranged from 1 to 10, where 1 represents the least attractive and 10 represents the most attractive. Multivariable logistic regression analysis was performed considering attractiveness scores, demographics, and professional characteristics. The primary outcome of interest was an invitation to an interview with a residency program. Results The residency program response rate was 47.5% (29/61). Among 2681 unique applications to 10 specialties in a single academic health system, the median attractiveness score for all applicants was 6.02 (IQR 5.54-6.55). The univariable analysis indicated a 19% higher invitation likelihood with a 1-point increase in attractiveness. After adjusting for demographics and professional experiences, the association lost statistical significance. Additional adjustment for United States Medical Licensing Examination scores further attenuated the association. Conclusions While higher attractiveness scores correlated with an increased likelihood of securing an interview, this correlation was not statistically significant after adjusting for other variables.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".