Synchronous Online Journal Club Connects Subspecialty Trainees Across Geographic Barriers
Bibliographic record
Abstract
Introduction: Journal club holds a well-respected place in medical education by promoting critical review of the literature and fostering scholarly discussions. Journal clubs are often not available to trainees with niche interests due to the geographic limitations of subspecialty programs such assimulation, medical education, disaster medicine, ultrasound, global health, and women’s health.Methods: A recurring online journal club was held on a quarterly basis to connect simulation fellows. An online conferencing program with screen-sharing capabilities served as the platform for this scholarly exchange. Articles were presented by fellows supported by more seasoned mentors. We surveyed participants to evaluate the program and provide feedback to the presenter.Results: The first eight sessions drew participants from across the United States and Canada. The program was highly rated by participants who commented specifically on its value. Presenters were also highly rated, suggesting that fellows, with online support and mentoring, were effective in providing a quality program.Conclusion: Online synchronous journal clubs can fill an educational niche for subspecialists andtheir trainees, as demonstrated with this curriculum piloted with simulation fellows. Challenges of scheduling across time zones, distribution of materials, and recruitment of participants can be overcome by a dedicated team of facilitators aided by readily accessible technology.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.063 | 0.009 |
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".