Are e-learning Webinars the future of medical education? An exploratory study of a disruptive innovation in the COVID-19 era
Bibliographic record
Abstract
OBJECTIVE: This study investigated the impact of the Webinar on deep human learning of CHD. MATERIALS AND METHODS: This cross-sectional survey design study used an open and closed-ended questionnaire to assess the impact of the Webinar on deep learning of topical areas within the management of the post-operative tetralogy of Fallot patients. This was a quantitative research methodology using descriptive statistical analyses with a sequential explanatory design. RESULTS: One thousand-three-hundred and seventy-four participants from 100 countries on 6 continents joined the Webinar, 557 (40%) of whom completed the questionnaire. Over 70% of participants reported that they "agreed" or "strongly agreed" that the Webinar format promoted deep learning for each of the topics compared to other standard learning methods (textbook and journal learning). Two-thirds expressed a preference for attending a Webinar rather than an international conference. Over 80% of participants highlighted significant barriers to attending conferences including cost (79%), distance to travel (49%), time commitment (51%), and family commitments (35%). Strengths of the Webinar included expertise, concise high-quality presentations often discussing contentious issues, and the platform quality. The main weakness was a limited time for questions. Just over 53% expressed a concern for the carbon footprint involved in attending conferences and preferred to attend a Webinar. CONCLUSION: E-learning Webinars represent a disruptive innovation, which promotes deep learning, greater multidisciplinary participation, and greater attendee satisfaction with fewer barriers to participation. Although Webinars will never fully replace conferences, a hybrid approach may reduce the need for conferencing, reduce carbon footprint. and promote a "sustainable academia".
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".