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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".