Physical Attractiveness and Chances of Being Invited to Interview With a Medical Residency Program: Retrospective Cohort Study
Bibliographic record
Abstract
<sec> <title>BACKGROUND</title> 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 one paper related to the residency match. </sec> <sec> <title>OBJECTIVE</title> This study further investigates the relationship between applicants’ 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 is enough data suggesting that ERAS photograph being visible prior to interview gives unfair advantage to more attractive applicants, this practice might be reconsidered by some residency programs or ERAS itself. </sec> <sec> <title>METHODS</title> Residency directors were surveyed on application review practices. Programs that view 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 utilized to determine attractiveness scores for ERAS photographs. The scores ranged from 1 to 10 where 1 is the least attractive and 10 is 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. </sec> <sec> <title>RESULTS</title> Residency program response rate was 47.5%. Among 2,681 unique applications to 10 specialties in a single academic health system, the median attractiveness score for all applicants was 6.02. 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. </sec> <sec> <title>CONCLUSIONS</title> While higher attractiveness scores correlated with increased likelihood of securing an interview, this correlation was not statistically significant after adjusting for other variables. </sec>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".