Upgrading a Social Media Strategy to Increase Twitter Engagement During the Spring Annual Meeting of the American Society of Regional Anesthesia and Pain Medicine
Bibliographic record
Abstract
Microblogs known as "tweets" are a rapid, effective method of information dissemination in health care. Although several medical specialties have described their Twitter conference experiences, Twitter-related data in the fields of anesthesiology and pain medicine are sparse. We therefore analyzed the Twitter content of 2 consecutive spring meetings of the American Society of Regional Anesthesia and Pain Medicine using publicly available online transcripts. We also examined the potential contribution of a targeted social media campaign on Twitter engagement during the conferences. The original Twitter meeting content was largely scientific in nature and created by meeting attendees, the majority of whom were nontrainee physicians. Physician trainees, however, represent an important and increasing minority of Twitter contributors. Physicians not in attendance predominantly contributed via retweeting original content, particularly picture-containing tweets, and thus increased reach to nonattendees. A social media campaign prior to meetings may help increase the reach of conference-related Twitter discussion.
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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 | 0.017 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".