Diversity and Inclusion in Internal Medicine Training Programs: An Unfulfilled Dream
Bibliographic record
Abstract
Background Promoting a diversified healthcare force fosters more culturally centered care, expands the approach to high-quality healthcare for poorly served populations, improves patient contentment, and broadens research agendas, all components essential to minimize healthcare imbalances. Our study reviews the trends of gender and racial disparity in Internal Medicine residency programs. Methodology In this retrospective analysis, we extracted data from the Accreditation Council for Graduate Medical Education's annual Data Resource Books from 2007 to 2019. Gender was reported as males and females. Race/ethnicity was cataloged as White/non-Hispanic, Black/non-Hispanic, Hispanic, Asian or Pacific Islander, Native American/Alaskan, others, and unknown. Results The representation of women increased progressively, with a relative increase of 4.7% from 2007 to 2019. For race/ethnicity, the study period started from the year 2011. When averaged across the eight-year study period, 27% of the study sample were White (non-Hispanic), followed by Asian/Pacific Islanders at 21%. The representation of other races was even lower. For 36.2% of the residents, the racial data were not known and categorized as unknown racial distribution. Conclusions Our study reports that gender and racial/ethnic imbalance persists within the training programs of Internal Medicine. Effectual strategies should be implemented to improve access to care to the underrepresented communities, address physician shortages in different areas of the country, and strengthen our ability to address long-established disparities in healthcare and outcomes.
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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 | 0.033 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.003 | 0.006 |
| 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".