Creating a Virtual Journal Club: A Community of Practice Using Multiple Social Media Strategies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.259 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it