Creating a Virtual Journal Club: A Community of Practice Using Multiple Social Media Strategies
Bibliographic record
Abstract
A journal club provides an opportunity to critically appraise the medical literature and apply it to clinical practice. Traditional, in-person journal clubs face challenges of scheduling participants and facilitators, recruiting local experts, and having a limited, local impact.Journal clubs may help develop communities of practice involving “groups of people who share a concern or a passion for something they do and learn how to do it better as they interact regularly.”1 With the advent of modern digital technologies, online medical-related journal clubs are increasing: participation can be synchronous or asynchronous, experts can be recruited from a global pool, and discussions are digitally archived for broader dissemination. In addition, these journal clubs may disseminate educational innovations and interventions to a wider audience for further study, and they provide rapid feedback to authors regarding similar work occurring elsewhere. These online discourses, however, typically incorporate a single social media strategy, such as Twitter-based journal clubs (#UroJC,2 #NephJC, http://www.nephjc.com).In an age where we view, engage, and learn from multiple digital streams, a virtual journal club requires a multimodal social media strategy to optimize reach and engagement. In January 2015, a virtual medical education journal club called “JGME-ALiEM Hot Topics in Medical Education” was piloted as a joint collaboration between the Journal of Graduate Medical Education and Academic Life in Emergency Medicine (ALiEM, an education blog with 1.2 million page views per year).3 This Rip Out describes how to move from hosting an online, single platform to a virtual, multimodal journal club by using a blog platform as the central repository of information to house blog comments, embedded Twitter comments, and embedded Google Hangouts on Air video discussions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.005 | 0.030 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".