Social Media in Heart Failure: A Mixed-Methods Systematic Review
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Among social media (SoMe) platforms, Twitter and YouTube have gained popularity, facilitating communication between cardiovascular professionals and patients. OBJECTIVE: This mixed-methods systematic review aimed to assess the source profile and content of Twitter and YouTube posts about heart failure (HF). METHODS: We searched PubMed, Embase and Medline using the terms "cardiology," "social media," and "heart failure". We included full-text manuscripts published between January 1, 1999, and April 14, 2019. We searched Twitter and YouTube for posts using the hashtags "#heartfailure", "#HF", or "#CHF" on May 15, 2019 and July 6, 2019. We performed a descriptive analysis of the data. RESULTS: Three publications met inclusion criteria, providing 677 tweets for source profile analysis; institutions (54.8%), health professionals (26.6%), and patients (19.4%) were the most common source profiles. The publications provided 1,194 tweets for content analysis: 83.3% were on education for professionals; 33.7% were on patient empowerment; and 22.3% were on research promotion. Our search on Twitter and YouTube generated 2,252 tweets and > 400 videos, of which we analyzed 260 tweets and 260 videos. Sources included institutions (53.5% Twitter, 64.2% You- Tube), health professionals (42.3%, 28.5%), and patients (4.2%, 7.3%). Content included education for professionals (39.2% Twitter, 62.3% YouTube), patient empowerment (20.4%, 21.9%), research promotion (28.8%, 13.1%), professional advocacy (5.8%, 2.7%), and research collaboration (5.8%, 0%). CONCLUSION: Twitter and YouTube are platforms for knowledge translation in HF, with contributions from institutions, health professionals, and less commonly, from patients. Both focus largely on education for professionals and less commonly on patient empowerment. Twitter includes more research promotion, research collaboration, and professional advocacy than YouTube.
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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 | 0.025 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.003 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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