Social Media in Heart Failure: A Mixed-Methods Systematic Review
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.033 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".