Skin of Colour Education Initiatives Among Dermatology Residents: A Narrative Review
Bibliographic record
Abstract
BACKGROUND: Achieving comprehensive education in skin of colour (SoC) dermatology presents a multifaceted challenge for dermatology trainees. Exposure to diverse skin tones in didactic curricula and clinical encounters varies greatly based on geographic location and institution and, as a whole, remains disparate. While SoC education initiatives for medical students and residents have increased in recent years, the characteristics and outcomes of initiatives specifically tailored to dermatology residents have not been summarized. OBJECTIVES: (1) Outline the demographic features of participants; (2) summarize intervention characteristics and the metrics by which educational impact was defined. METHODS: MEDLINE, Embase, and PubMed were searched for SoC education interventions aimed at dermatology trainees. RESULTS: Five studies were selected for inclusion. Two hundred thirty-seven dermatology residents participated from institutions across the United States, United Kingdom, Australia, and Botswana. Most interventions were didactics based, assessed changes in subjective confidence, and employed identical pre- and posttest questions. Confidence increased for didactic-only interventions, decreased with multimodal interventions, and was incongruent with objective knowledge or diagnostic scores. Single-format interventions or assessments with identical pre- and posttest questions may provide an inflated sense of confidence through recall bias or other heuristics. Conversely, the cognitive synthesis afforded by multimodal interventions or new (but equivalent) assessment questions may lead to low confidence ratings despite improved knowledge scores. CONCLUSIONS: When designing SoC learning initiatives in postgraduate dermatology education, multimodal formats, and paired objective and subjective assessments that employ both identical and different pre- and post-intervention questions may give a more relevant and accurate reflection of impact in clinical practice settings.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".