Teaching Dermatology to Internal Medicine Residents: Needs Assessment Survey and Possible Directions
Bibliographic record
Abstract
BACKGROUND: Internal medicine trainees receive limited teaching and training in dermatology and may feel inadequately prepared to assess and manage patients with dermatologic complaints. No study to date has assessed the needs of internal medicine trainees in Canada with regard to dermatology teaching. OBJECTIVE: To determine internal medicine residents' comfort in assessing and managing dermatologic issues and their educational needs in dermatology. METHODS: An electronic survey was conducted of first-, second-, and third-year internal medicine residents at the University of Toronto. RESULTS: Fifty-four of 186 internal medicine trainees responded to our survey (response rate = 29%). Each respondent did not answer every question. Residents were generally uncomfortable or very uncomfortable assessing and managing dermatologic issues in the emergency department (40 of 47, 85%), ward or intensive care unit (39 of 47, 83%), and ambulatory clinic (40 of 47, 85%). Residents thought that various clinical and didactic dermatology exposures would be useful to their training as internists. Case-based teaching and ambulatory clinical rotations were felt to be particularly valuable. Additionally, 38 of 46 (83%) respondents wanted to learn how to perform punch biopsies. CONCLUSIONS: An effort should be made to increase the availability of relevant dermatology teaching and clinical exposures for internal medicine residents.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".