Current landscape and future directions: a cross-sectional study of diversity among dermatology leadership in Canada
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Medical leadership and education which reflects the diversity of patient populations are crucial to equitable healthcare experiences and outcomes. This study aims to assess the current landscape of diversity in dermatology leadership and educational curricula in Canada. We also sought to collect and summarise recommendations made by current dermatology leaders about how to improve diversity in the field. METHODS: This cross-sectional study assessed the self-reported racial/ethnic backgrounds and Fitzpatrick skin types of Canadian dermatology leaders. Individuals who held one or more leadership positions in the past 10 years were identified and sent an anonymous, online survey. Respondent's demographic information and perspectives on diversity in dermatology were analysed with proportions and thematic analysis, respectively. RESULTS: The survey response rate was 50.0% (55/110). 65.5% (36/55) of respondents identified as White/Caucasian. More respondents identified as having Fitzpatrick skin types 1-2 (65.5%) compared with Fitzpatrick skin types 3-6 (34.5%). More respondents (68.9%) holding leadership positions in national, provincial or regional societies identified as White/Caucasian compared with leaders in academic or research roles (56.5%). Most respondents believed that Black, Indigenous and people of colour are not sufficiently represented in Canadian dermatology leadership and that skin of colour is not adequately represented in dermatology educational curricula. CONCLUSIONS: Our study suggests a potential underrepresentation of racial and ethnic minorities in Canadian dermatologists in national, provincial and regional society leadership positions. Most Canadian dermatologists previously or currently holding leadership roles believe that further efforts are necessary to improve equity, diversity and inclusion in the field.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 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".