Joint clinical commitment will advance integration of telehealth, value of patient care
- Bias Rating
-78% Very Liberal
- Reliability
80% ReliableGood
- Policy Leaning
-78% Very Liberal
- Politician Portrayal
N/A
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By creating an account, you agree to our Terms and Privacy Policy, and subscribe to email updates. Already a member: Log inBias Score Analysis
The A.I. bias rating includes policy and politician portrayal leanings based on the author’s tone found in the article using machine learning. Bias scores are on a scale of -100% to 100% with higher negative scores being more liberal and higher positive scores being more conservative, and 0% being neutral.
Sentiments
64% Positive
- Liberal
| Sentence | Sentiment | Bias |
|---|---|---|
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Reliability Score Analysis
Policy Leaning Analysis
Politician Portrayal Analysis
Bias Meter
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-100%
Liberal
100%
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Contributing sentiments towards policy:
75% : Through its commitment to rigorous research and innovation, ViVE is transforming virtual care, accelerating translational research from the lab to the world.60% : Supported by more than 35 million volunteers globally, we fund groundbreaking research, advocate for the public's health, and provide critical resources to save and improve lives affected by cardiovascular disease and stroke.
58% : "Our ultimate goal is to advance translational research -- bridging the gap between lab discoveries and real-world impact for patients and health care providers in urban and rural settings," Khairat said.
55% : Connect with us on heart.org, Facebook, X or by calling 1-800-AHA-USA1. About the National Center for Clinical and Translational Science funded Center for Virtual Care Value and Excellence The National Center for Clinical and Translational Science (NCATS) funded the Center for Virtual Care Value and Excellence (ViVE) spearheads translational research and innovation in virtual care by optimizing value-driven care and promoting excellence.
*Our bias meter rating uses data science including sentiment analysis, machine learning and our proprietary algorithm for determining biases in news articles. Bias scores are on a scale of -100% to 100% with higher negative scores being more liberal and higher positive scores being more conservative, and 0% being neutral. The rating is an independent analysis and is not affiliated nor sponsored by the news source or any other organization.
