Twitter-based journal club as a global medical education tool - Insights from the IPNA Journal Club (#IPNAJC)
Bibliographic record
Abstract
Abstract Free Open Access Medical Education (FOAMed) is successfully utilized by medical professionals worldwide to improve educational equity and networking opportunities. One such novel FOAMed tool is a Twitter-based journal club, #IPNAJC. The summary and infographics of selected pediatric nephrology article(s) are published on the IPNAJC website and emailed to members. Two separate, live, one-hour sessions are conducted in major international time zones in English and Spanish. Authors and experts are invited to the discussion. After #IPNAJC, a wrap-up is distributed across the IPNAJC membership via an email¸ for access on demand. An online, anonymous 11-question survey was distributed to IPNA members by email between Nov–Dec 2021. The response rate was 3.5 % (n=67). Most responses were from Asia (33%) and physicians (72%). Participants learned about #IPNAJC via email (69%), social media (17%), and colleagues (14%). Approximately 42% participated live; the remaining interacted with the materials asynchronously. The median (IQR) overall quality was 4 (3–5), the quality of summaries was 4 (4–5), VA usefulness was 5 (5), and ease of participation in the discussion was 5 (3–5). Analysis of Twitter chat revealed that 24 people participated in the live #IPNAJC, generating 424 tweets and 1.048 million impressions (median (IQR)). Most respondents perceived that the #IPNAJC and its FOAMed resources were of good quality. This survey study reinforces that FOAMed resources have a broad geographical reach, are accessed by users at all career stages, and can be utilized during and after a social media event.
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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 | 0.007 | 0.020 |
| 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 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".