Adverse Childhood Experiences, Household Income, and Mentorship Among Interns Who Are Underrepresented in Medicine
Bibliographic record
Abstract
Background: Underrepresented in medicine (UIM) interns have unique lived experiences that affect their paths to medicine, and more information is needed for medical residency and fellowship programs to better support them. Objective: We describe self-reported differences between UIM and White physician interns in key demographic areas, including household income growing up, physician mentorship, and adverse childhood experiences (ACEs). Methods: Between 2019 and 2021, we administered a diversity survey to incoming medical interns at the University of Minnesota-Twin Cities. Response rates across the 3 years were 51.2% (167 of 326), 93.9% (310 of 330), and 98.9% (354 of 358), respectively. We conducted analyses to compare UIM and White groups across demographic variables of interest. Results: A total of 831 of 1014 interns (81.9%) completed the survey. Relative to White interns, UIM interns had lower household incomes growing up, lower rates of mentorship, and higher rates of experiencing 4 or more ACEs. The odds of experiencing the cumulative burden of having a childhood household income of $29,999 or less, no physician mentor, and 4 or more ACEs was approximately 10 times higher among UIM (6.41%) than White (0.66%) interns (OR=10.38, 95% CI 1.97-54.55). Conclusions: Childhood household income, prior mentorship experiences, and number of ACEs differed between UIM and White interns.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".