The Impact of Multicultural Interfacility Video Case Conference: A Novel Education Model After the COVID Pandemic
Bibliographic record
Abstract
Context: The COVID-19 pandemic challenged undertaking gradual educational activities for residency and fellowship trainees. However, recent technological advances have enabled broadening active learning opportunities through international online conferences. Objective: The format of our international online endocrine case conference, launched during the pandemic, is introduced. The objective impact of this program on trainees is described. Methods: Four academic facilities developed a semiannual international collaborative endocrinology case conference. Experts were invited as commentators to facilitate in-depth discussion. Six conferences were held between 2020 and 2022. After the fourth and sixth conferences, anonymous multiple-choice online surveys were administered to all attendees. Results: Participants included trainees and faculty. At each conference, 3 to 5 cases of rare endocrine diseases from up to 4 institutions were presented, mainly by trainees. Sixty-two percent of attendees reported 4 facilities as the appropriate size for the collaboration to maintain active learning in case conferences. Eighty-two percent of attendees preferred a semiannual conference. The survey also revealed the positive impact on trainees' learning regarding diversity of medical practice, academic career development, and confidence in honing of presentation skills. Conclusion: We present an example of our successful virtual global case conference to enhance learning about rare endocrine cases. For the success of the collaborative case conference, we suggest smaller cross-country institutional collaborations. Preferably, they would be international, semiannually based, and with recognized experts as commentators. Since our conference has engendered multiple positive effects on trainees and faculty, continuation of virtual education should be considered even after the pandemic era.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".