Defining a Role for Webinars in Surgical Training Beyond the COVID-19 Pandemic in the United Kingdom: Trainee Consensus Qualitative Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic posed several challenges for surgical training, including the suspension of many in-person teaching sessions in lieu of webinars. As restrictions have eased, both prepandemic and postpandemic training methods should be used. OBJECTIVE: This study investigates trainees' experiences of webinars during the COVID-19 pandemic to develop recommendations for their effective integration into surgical training going forward. METHODS: This project was led by the Association of Surgeons in Training and used an iterative process with mixed qualitative methods to consolidate arguments for and against webinars, and the drivers and barriers to their effective delivery, into recommendations. This involved 3 phases: (1) a web-based survey, (2) focus group interviews, and (3) a consensus session using a nominal group technique. RESULTS: Trainees (N=281) from across specialties and grades confirmed that the COVID-19 pandemic led to an increase in webinars for surgical training. While there were concerns, particularly around the utility for practical training (80.9%), the majority agreed that webinars had a role in training following the COVID-19 pandemic (90.2%). The cited benefits included improved access or flexibility and potential standardization of training. The majority of limitations were technical. These perspectives were refined through focus group interviews (n=18) into 25 recommendations, 23 of which were ratified at a consensus meeting, which was held at the Association of Surgeons in Training 2021 conference. CONCLUSIONS: Webinars have a role in surgical training following the COVID-19 pandemic. The 23 recommendations encompass indications and technical considerations but also discuss important knowledge gaps. They should serve as an initial framework for ensuring that webinars add value and continue to evolve as a tool for training. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2200055325; http://www.chictr.org.cn/showprojen.aspx?proj=142802.
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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 | 0.051 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| 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".