Diversity in Dermatology? An Assessment of Undergraduate Medical Education
Bibliographic record
Abstract
Background A lack of representation of skin of color (SoC) in dermatology curricula is well-documented across North American medical schools and may present a barrier to equitable and comprehensive undergraduate medical education. Objectives This study aims to examine representation in dermatologic educational materials and appreciate a link between bias in dermatologic education and student diagnostic ability and self-rated confidence. Design The University of Toronto Dermatology Undergraduate Medical Education curriculum was examined for the percentage photographic representation of SoC. A survey of 10 multiple-choice questions was administered to first- and third-year medical students at the University of Toronto to assess diagnostic accuracy and self-rated confidence in diagnosis of 5 common skin lesions in Fitzpatrick skin phototypes (SPT) I-III (white skin) and VI-VI (SoC). Results The curriculum audit showed that <7% of all images of skin disease were in SoC. Diagnostic accuracy was fair for both first- (77.8% and 85.9%) and third-year (71.3% and 72.4%) cohorts in white skin and SoC, respectively. Students’ overall self-rated confidence was significantly greater in white skin when compared to SoC, in both first- (18.75/25 and 17.78/25, respectively) and third-year students (17.75/25 and 15.79/25, respectively) ( P = .0002). Conclusions This preliminary assessment identified a lack of confidence in diagnosing dermatologic conditions in SoC, a finding which may impact health outcomes of patients with SoC. This project is an important first step in diversifying curricular materials to provide comprehensive medical education.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".