Knowledgeof Dermatology Residents on the COVID-19 Pandemic: A Cross-Sectional Study
Bibliographic record
Abstract
Background: A novel coronavirus disease (COVID-19) has spread throughout the world leading to a global pandemic. As a result, all healthcare workers have been profoundly affected. Objectives: The goal of our study is to identify the level of knowledge and the effect of COVID-19 on dermatology residents. Methods: A cross-sectional analysis in which 77 dermatology residents from three Gulf Cooperation Council (GCC) countries and Canada completed an online questionnaire-based survey. The questionnaire consisted of four sections: one general information about the resident and three on knowledge, safety measures and impact of COVID-19, with a total of 26 questions. The questionnaire was scored out of 10 with those above the mean considered as having satisfactory knowledge. Results: The mean (SD) knowledge score was 6.25 (1.6). There was a statistically significant difference noted between the GCC countries and Canada in terms of the knowledge score (p-value=0.035). Only 14% of dermatology residents felt competent in managing COVID-19 patients. Seventy percent felt that the pandemic has negatively affected their dermatology training. Conclusion: Dermatology residents demonstrated a difference in knowledge score in relation to the geographic location of the program. Almost 46% of residents illustrated a satisfactory knowledge score about COVID-19. Only a small percentage of residents are confident in treating COVID-19 patients. Subsequently, the need for improved education of residents regarding COVID-19 before redeployment is warranted.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".