Five years of #MedRadJClub: An impact evaluation of an established twitter journal club
Bibliographic record
Abstract
INTRODUCTION: Twitter journal clubs are a relatively new adaptation of an established continuing professional development (CPD) activity within healthcare. The medical radiation science (MRS) journal club 'MedRadJClub' (MRJC) was founded in March 2015 by a group of academics, researchers and clinicians as an international forum for the discussion of peer-reviewed papers. To investigate the reach and impact of MRJC, a five-year analysis was conducted. METHODS: Tweetchat data (number of participants, tweets and impressions) for the first five years of MRJC were extracted and chat topics organised into themes. Fifth anniversary MRJC chat tweets were analysed and examples of academic and professional outputs were collated. RESULTS: A total of 59 chats have been held over five years with a mean of 41 participants and 483,000 impressions per hour-long synchronous chat. Ten different tweetchat themes were identified, with student engagement/preceptorship the most popular. Eight posters or oral presentations at conferences, one social media workshop and four papers have been produced. Qualitative analysis revealed five core themes relating to the perceived benefits of participation in MRJC: (1) CPD and research impact, (2) professional growth and influencing practice, (3) interdisciplinary learning and inclusion, (4) networking and social support and (5) globalisation. CONCLUSION: MRJC is a unique, multi-professional, global community with consistent engagement. It is beneficial for both CPD, research engagement, dissemination and socialisation within the MRS community.
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How this classification was reachedexpand
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 | 0.052 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".