Teledermatology: Canadian dermatologists’ practice patterns, perceived challenges and future recommendations
Bibliographic record
Abstract
The use of teledermatology has increased significantly in recent years. The objective of this study was to determine Canadian dermatologists' and dermatology residents' perspectives on teledermatology. An online survey was created to determine participants' teledermatology practice patterns and their perception of the challenges, education, training, and research in teledermatology. The survey was distributed through the Canadian Dermatology Association and by administrative staff at Canadian Dermatology departments. A total of 33 respondents completed the survey: 66.7% of respondents started using teledermatology during the COVID-19 pandemic, and 93.8% reported that teledermatology accounted for 0-25% of their practice. Convenience, access, and safety were identified as the primary advantages of teledermatology. Teledermatology was mainly utilized for medication monitoring or refills and to assess, manage, or follow up on dermatitis, other chronic inflammatory conditions, and pre-diagnosed dermatologic conditions. Poor photo quality (32.3%) and the inability to conduct physical examinations or accurately diagnose conditions (74.2%) were reported as significant challenges. Respondents recommended education on the medicolegal considerations of teledermatology and research on how teledermatology compares to in-person consultations. Overall, teledermatology improves convenience, access to care, and safety for both patients and healthcare professionals. However, addressing challenges related to physical examinations, accurate diagnoses, and photo quality is essential for optimal care delivery.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".