ASPChat: Participation and Reach of a Real-Time Twitter Chat on Antimicrobial Stewardship
Bibliographic record
Abstract
Background: Healthcare professionals with roles in infectious disease and antimicrobial stewardship have a growing presence on social media. Twitter has evolved to become a popular venue for healthcare professional communication, with the potential to support improved quality of patient care. To harness this growth and provide an opportunity for learning and networking, we developed a monthly Twitter chat on a variety of antimicrobial stewardship topics. Our objective was to evaluate the reach of this online initiative. Methods: In November 2016, to coincide with World Antibiotic Awareness Week, we held the first ASP chat (#ASPChat). Twitter chats continue monthly for 1 hour each month. Topics range from rapid diagnostic testing to duration of antibiotic therapy, and 6 questions are posed for each event. Questions about common strategies, clinical pearls, helpful resources, and literature are commonly integrated into the discussion. The event is open to all Twitter users regardless of discipline or location of practice. Participants use the ASPChat hash tag to follow along with the conversation. To evaluate the monthly Twitter chats, analytics were obtained from Symplur Healthcare Hashtags including impressions, the number of potential views for each Tweet, number of Tweets, and number of participants. Results: To date, 33 ASPChat events have been held, with a total of 20,478,000 impressions. The average number of Tweets per month was 346 and the average number of participants was 86 (Fig. 1). Participants have included pharmacists, physicians, infection control practitioners, and nonclinicians. Countries represented have included the United States, Canada, the United Kingdom, Australia, New Zealand, and South Africa. The average monthly impressions stands at 620,559 and has increased each year from between 23% and 86%. Conclusions: A monthly Twitter Chat is a feasible and sustainable approach to connecting antimicrobial stewards across a wide geographical range. The broad reach of the ASPChat events presents an opportunity to influence and unite a diverse group of professionals aiming to improve antibiotic use. Further evaluation is recommended to understand the professional and clinical impact of this important communication tool. Funding: None Disclosures: None
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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; a candidate call from one teacher head, 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".