Medical school dermatology education: a scoping review
Bibliographic record
Abstract
Dermatological diseases are widespread and have a significant impact on the quality of life of patients; however, access to appropriate care is often limited. Improved early training during medical school represents a potential upstream solution. This scoping review explores dermatology education during medical school, with a focus on identifying the factors associated with optimizing the preparation of future physicians to provide care for patients with skin disease. A literature search was conducted using online databases (Embase, MEDLINE, CINAHL and Scopus) to identify relevant studies. The Joanna Briggs Institute methodological framework for scoping reviews was used, including quantitative and qualitative data analysis following a grounded theory approach. From 1490 articles identified, 376 articles were included. Most studies were from the USA (46.3%), UK (16.2%), Germany (6.4%) and Canada (5.6%). Only 46.8% were published as original articles, with a relatively large proportion either as letters (29.2%) or abstracts (12.2%). Literature was grouped into three themes: teaching content, delivery and assessment. Core learning objectives were country dependent; however, a common thread was the importance of skin cancer teaching and recognition that diversity and cultural competence need greater fostering. Various methods of delivery and assessment were identified, including computer-aided and online, audiovisual, clinical immersion, didactic, simulation and peer-led approaches. The advantages and disadvantages of each need to be weighed when deciding which is most appropriate for a given learning outcome. The broader teaching-learning ecosystem is influenced by (i) community health needs and medical school resources, and (ii) the student and their ability to learn and perform. Efforts to optimize dermatology education may use this review to further investigate and adapt teaching according to local needs and context.
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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 | 0.011 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.020 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".