Teledermatology Utilization and Integration in Residency Training Over the COVID-19 Pandemic
Bibliographic record
Abstract
BACKGROUND: During the 2019 Coronavirus (COVID-19) pandemic, the Division of Dermatology, University of Ottawa, adapted pre-existing local healthcare infrastructures to provide increased provider-to-provider teledermatology services as well as integrated teledermatology into the dermatology residency training program. OBJECTIVES: (1) To assess the differences in utilization of provider-to-provider teledermatology services before and during the COVID-19 pandemic; and (2) to assess dermatology resident and faculty experiences with the integration of teledermatology into dermatology residency training at the University of Ottawa. METHODS: We conducted a cross-sectional analysis comparing provider-to-provider teledermatology consults submitted to dermatologists from April 2019 to October 2019 pre-pandemic with the same period during the pandemic in 2020. Two different questionnaires were also disseminated to the dermatology residents and faculty at our institution inquiring about their perspectives on teledermatology, education, and practice. RESULTS: The number of dermatologists completing consults, the number of providers submitting a case to Dermatology, and the number of consults initiated all increased during the pandemic period. Ninety-one percent of residents agreed that eConsults and teledermatology enhanced their residency education, enabled continuation of training during the pandemic, and that eConsult-based training should be incorporated into the curriculum. Ninety-six percent of staff incorporated a virtual dermatology practice model, and one-third used teledermatology with residents during the pandemic. Most staff felt there was value in providing virtual visits in some capacity during the pandemic. CONCLUSIONS: Our study confirms that the use of teledermatology services continues to increase accessibility during the pandemic. Teledermatology enhances the education and training of residents and will be incorporated into dermatology residency programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".