Aesthetic Dermatologic Surgery Training in Canadian Residency Programs
Bibliographic record
Abstract
BACKGROUND:: The public and other physicians expect dermatologists to be experts on aesthetic dermatology services. In Canada, current challenges may limit residents from achieving competency in aesthetic dermatology during their training. This may adversely affect patient safety, create medicolegal risks, and deter graduates from offering aesthetic procedures. OBJECTIVES:: The objective of this article is to characterize the curriculum, hands-on learning opportunities, and perceptions of aesthetic dermatologic training in Canadian dermatology residency training programs. METHODS:: An online survey of faculty and residents within Canadian dermatology residency programs was performed. The main outcome measures were the hours of formal aesthetic dermatology teaching, the frequency of hands-on dermatology resident training with injectables and devices, and comparing faculty and resident perspectives regarding resident aesthetic dermatology training. RESULTS:: Thirty-six faculty members (40%) and 47 residents (34%) responded to the survey. Lasers, fillers, neuromodulators, and mole removal were most commonly taught in the 10 hours or fewer of formal instruction. Residents commonly observed rather than performed procedures. High dissatisfaction among residents was reported with the quality and quantity of aesthetic dermatology training. Faculty and resident respondents supported increasing aesthetic dermatology education, and approximately 70% of residents plan to offer aesthetic services. Discounted pricing or resident-led clinics were felt to be ways to increase resident hands-on experience. CONCLUSIONS:: The standardization of core competencies in aesthetic dermatologic procedures is essential to ensure patient safety and practitioner competence. At present, formal aesthetic dermatology training in residency may be insufficient for hands-on training. The majority of dermatology faculty and resident respondents support increasing aesthetic dermatology training.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".