Evidence-based Guidelines on the Use of Virtual Surgical Education Pertaining to the Domains of Cognition and Curriculum, Psychomotor Skills Training, and Faculty Development and Mentorship
Bibliographic record
Abstract
OBJECTIVE: To identify, categorize, and evaluate the quality of literature, and to provide evidence-based guidelines on virtual surgical education within the cognitive and curricula, psychomotor, and faculty development and mentorship domains. SUMMARY OF BACKGROUND DATA: During the coronavirus disease 2019 pandemic, utilizing virtual learning modalities is expanding rapidly. Although the innovative methods must be considered to bridge the surgical education gap, a framework is needed to avoid expansion of virtual education without proper supporting evidence in some areas. METHODS: The Association for Surgical Education formed an ad-hoc research group to evaluate the quality and methodology of the current literature on virtual education and to build evidence-based guidelines by utilizing the SiGN methodology. We identified patient/problem-intervention-comparison-outcome-style questions, conducted systematic literature reviews using PubMed, EMBASE, and Education Resources information Center databases. Then we formulated evidence-based recommendations, assessed the quality of evidence using Grading of Recommendations, Assessment, Development, and Evaluation, Newcastle-Ottawa Scale for Education, and Kirkpatrick ratings, and conducted Delphi consensus to validate the recommendations. RESULTS: Eleven patient/problem-intervention-comparison-outcome-style questions were designed by the expert committees. After screening 4723 articles by the review committee, 241 articles met inclusion criteria for full article reviews, and 166 studies were included and categorized into 3 domains: cognition and curricula (n = 92), psychomotor, (n = 119), and faculty development and mentorship (n = 119). Sixteen evidence-based recommendations were formulated and validated by an external expert panel. CONCLUSION: The evidence-based guidelines developed using SiGN methodology, provide a set of recommendations for surgical training societies, training programs, and educators on utilizing virtual surgical education and highlights the area of needs for further investigation.
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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 | 0.073 | 0.225 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.013 |
| Bibliometrics | 0.024 | 0.013 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.010 | 0.006 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".