Educational Impact of #IDJClub, a Twitter-Based Infectious Diseases Journal Club
Bibliographic record
Abstract
BACKGROUND: Journal clubs have been an enduring mainstay of medical education, and hosting these on social media platforms can expand accessibility and engagement. We describe the creation and impact of #IDJClub, an infectious diseases (ID) Twitter journal club. METHODS: We launched #IDJClub in October 2019. Using the account @IDJClub, an ID physician leads a 1-hour open-access Twitter discussion of a recent publication. All participants use the hashtag #IDJClub. Sessions started monthly, but increased due to demand during the coronavirus disease 2019 (COVID-19) pandemic. We used Symplur 's Healthcare Hashtag project to track engagement 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: In its first 20 months, 31 journal clubs were held, with medians of 42 (interquartile range [IQR], 28.5-60) participants and 312 (IQR, 205-427.5) tweets per session. 134 participants completed the survey, of whom 39% were ID physicians, 19% pharmacists, 13% ID fellows, and 10% medical residents. Most agreed or strongly agreed that #IDJClub provided clinically useful knowledge (95%), increased personal confidence in independent literature appraisal (72%), and was more educational than traditional journal clubs (72%). The format addressed several barriers to traditional journal club participation such as lack of access, subject experts, and time. CONCLUSIONS: #IDJClub is an effective virtual journal club, providing an engaging, open-access tool for critical literature appraisal that overcomes several barriers to traditional journal club participations while fostering connectedness within the global ID community.
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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 | 0.017 |
| 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 | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".