Gynecologic Pathology Journal Club: A 2-year, Worldwide Virtual Learning Experience With a Focus on Mentorship and Inclusion
Bibliographic record
Abstract
Journal clubs (JCs) are a common format used in teaching institutions to promote trainee engagement and develop skills in seeking out evidence-based medicine and critically evaluating literature. Digital technology has made JC accessible to worldwide audiences, which allows for increased inclusion of globally diverse presenters and attendees. Herein we describe the experience of the first 2 years of a virtual gynecologic pathology JC designed with the goal of providing mentorship and increasing inclusivity. JC began in a virtual format in April 2020 in response to the need for remote learning during the coronavirus disease 2019 pandemic. Each JC had 1 moderator, lasted 1 hour, featured up to 3 trainees/early-career pathologists, and covered articles on gynecologic surgical pathology/cytopathology. Trainees were recruited through direct contact with moderators and advertising through social media (eg, Twitter). A template was used for all presentations, and before presenting, live practice sessions were conducted with the moderator providing constructive feedback and evaluations were provided to presenters and attendees for feedback. Recordings of the meetings were made publicly available after the event through YouTube, a society website, and emails to registrants. Fifty-nine presenters participated, covering 71 articles. Most were trainees (53/59; 89%) from North America (33/59; 56%), with additional presenters from Asia (14/59; 24%), Australia/Oceania (5/59; 8%), Africa (4/59; 7%), and Europe (3/59; 5%). An average of 20 hours were spent per month by moderators on the selection of papers, meeting preparation, and provision of mentorship/feedback. Live events had a total of 827 attendees, and 16,138 interactions with the recordings were noted. Among those who self-identified on provided surveys, the attendees were most commonly from Europe (107/290; 37%) and were overwhelmingly practicing pathologists (275/341; 81%). The experience, including mentorship, format, and content, was positively reviewed by attendees and presenters. Virtual JC is an inclusive educational opportunity to engage trainees and early-career pathologists from around the world. The format allowed for the JC to be widely viewed by attendees from multiple countries, most being practicing pathologists. Based on feedback received, virtual JC appears to expand the medical knowledge of the attendees and empower presenters to develop their expertise and communication skills.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.008 |
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".