135. Impact of #idjclub, a Synchronous Twitter Journal Club, as a Novel Infectious Disease Education Platform
Bibliographic record
Abstract
Abstract Background Journal clubs have been a mainstay of medical education since the days of Osler. Social media platforms allow virtual journal clubs to connect global participants. We describe the creation and impact of #IDJClub, an Infectious Diseases (ID) Twitter journal club. Methods We launched #IDJClub in October 2019. The format presents a recent ID publication for a 1-hour synchronous Twitter chat led by an ID physician from @IDJClub. Sessions started monthly, but increased in frequency due to interest during the COVID-19 pandemic. Pre-scripted tweets guide participants through the article description and analysis. We used Symplur’s Healthcare Hashtag project to track the number of impressions, tweets, participants, and the engagement rate (average tweets/participant) of #IDJClub per 60 minute discussion plus the following 30 minutes to capture ongoing conversations. We also conducted an online anonymous survey using Likert scales and open-ended questions to assess educational impact. Results As of June 11 2020, @IDJClub garnered 5,338 followers from around the world (Figure 1). In its first 9 months, 12 virtual journal clubs were conducted with a mean of 791,624 impressions, 328 tweets, and 48 participants per session, which steadily increased over time (Figure 2). A total of 134 participants completed the survey, of whom 40% were ID physicians, 19% pharmacists, 13% ID fellows, and 10% medical residents. Most respondents followed 1–2 (38%) or 3–4 (38%) of the discussions, with variable levels of active participation. Majorities agreed that #IDJClub provided clinically useful knowledge, increased personal confidence in review of literature, and compared favorably with in-person journal clubs (Figure 3). The format addressed several barriers such as lack of access to in-person journal clubs or subject experts at one’s own institution and lack of time to read new research or attend traditional journal clubs (Figure 4). Conclusion #IDJClub is an effective platform for virtual journal club, providing an engaging, open-access tool for critical appraisal of ID literature. This innovation in medical education overcomes several barriers to traditional journal clubs while fostering professional relationships within the global ID community. Disclosures Todd P. McCarty, MD, Amplyx (Scientific Research Study Investigator)Cidara (Scientific Research Study Investigator)
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".