Beyond Skin Deep: case-based online modules to teach multidisciplinary care in dermatology among clerkship students
Bibliographic record
Abstract
BACKGROUND: Canadian medical schools offer limited clinical dermatology training. In addition, there is a lack of educational resources that are designed specifically for clerkship students that focus on the multidisciplinary nature of dermatology. OBJECTIVES: After developing case-based educational resources to address the lack of clinical exposure and learning of multidisciplinary care in dermatology, this study aimed to evaluate the educational intervention and gather feedback for future module development. METHODS: Ten online interactive dermatology case-based modules involving 14 other disciplines were created. Medical students (n = 89) from two Canadian schools were surveyed regarding perceptions of the existing dermatology curriculum. Among 89 students, 46 voluntarily completed the modules, and a survey (a five-point Likert scale ratings) including narrative feedback was provided to determine an improvement in dermatology knowledge and understanding of multidisciplinary care. RESULTS: Among 89 surveyed students, only 17.1% agreed that their pre-clerkship dermatology education was sufficient and 10.2% felt comfortable managing patients with skin conditions in a clinical setting. Among 46 students, 95.7% of students agreed that the modules fit their learning style (4.17 ± 0.73 on Likert scale) with positive narrative feedback. 91.3% agreed or strongly agreed that the modules enhanced their dermatology knowledge (4.26 ± 0.61). 79.6% of students agreed that the modules helped with understanding the multidisciplinary nature of dermatological cases (3.98 ± 0.81). Student comfort to manage skin conditions increased 7.7 times from 10.2% to 78.3% post-module. CONCLUSIONS: Clerkship students had limited knowledge of dermatologic conditions; the case-based modules were able to successfully address these deficits and assist students in understanding the multidisciplinary nature of dermatology.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".