Skin of Colour Dermatoses Self-Learning Module: Assessing Student Comprehension
Bibliographic record
Abstract
BACKGROUND: Dermatology for diverse skin types is a globally growing area of medicine, but the inclusion of skin of color dermatology has not yet been formally included across all Canadian undergraduate medical education curricula. There is also a paucity of representation of diverse skin types in most medical textbooks, research, and clinical trials. OBJECTIVES: The main objective was to develop a concise, Skin of Colour Dermatoses Self-Learning Module (SOCSLM) that could be implemented at an undergraduate medical education level. The secondary objective was to analyze participant responses to improve and add to learning module content. METHODS: From March to May 2022, second-year medical students at the University of Ottawa completed pre- and post-SOCSLM questionnaires which were available in French and English through their online student learning portals. The pre-test consisted of five multiple choice questions relating to images of dermatoses seen in diverse skin types. The post-test repeated the same five questions, rearranged, with an additional five new ones, and responses were analyzed. RESULTS: Twenty-five participants completed the surveys, and twenty responses were included. Percent correct answers increased between pre- and post-test, 51% vs 87%. In the post-test, questions repeated from the pre-test had a mean score of 95% while the new post-test questions had a mean score of 80%. Interest in dermatology did not have an impact on correct response rates. CONCLUSIONS: Skin of color dermatology self-learning modules may be an effective way to integrate skin of color dermatology into undergraduate medical curricula.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".