Early Introduction of Dermatology Clinical Skills in Medical Training
Bibliographic record
Abstract
BACKGROUND: Skin diseases are among the most common diseases encountered by healthcare professionals. Despite this, dedicated dermatology teaching is limited in most Canadian medical school training programs. This is especially true of clinical skills training. OBJECTIVES: To determine the impact of early introduction of dermatology clinical skills in Canadian undergraduate medical training. Secondary objectives included examining the impact of having sessions co-led by a clinician with expertise in the field of dermatology. METHODS: A half-day dermatology clinical skills session was introduced in the University of Toronto preclerkship medical school during the dermatology week. Sessions were co-led by staff or resident dermatologists. Sessions were evaluated using student pre- and postclinical skills test scores, as well as experience questionnaires completed by both tutors and students. RESULTS: The clinical skills session was well received by both students and tutors, with no cumulative score less than 4.34 on a 5-point Likert scale. The majority of students agreed or strongly agreed that the introduction of clinical skills in the early years of the curriculum facilitated learning consolidation (99.5%). Comparison of pre- and post-test scores showed that students' dermatology comprehension improved by an average of 12.1% following completion of the session. In questionnaire responses, students and nondermatology co-tutors emphasized the impact of having an expert in the field of dermatology co-lead the session. CONCLUSIONS: Students valued early clinical skills exposure to dermatology and felt it enhanced their knowledge and abilities. This study demonstrates learning consolidation when dermatologic didactic teachings are paired with practical clinical skills sessions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".