
We all know the crisis in UK social care damages lives and the economy: it's the Treasury we must convince | Layla Moran
- Bias Rating
-60% Medium Liberal
- Reliability
60% ReliableAverage
- Policy Leaning
-60% Medium Liberal
- Politician Portrayal
N/A
Continue For Free
Create your free account to see the in-depth bias analytics and more.
Continue
Continue
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
32% Positive
- Liberal
- Conservative
Sentence | Sentiment | Bias |
---|---|---|
Unlock this feature by upgrading to the Pro plan. |
Reliability Score Analysis
Policy Leaning Analysis
Politician Portrayal Analysis
Bias Meter
Extremely
Liberal
Very
Liberal
Moderately
Liberal
Somewhat Liberal
Center
Somewhat Conservative
Moderately
Conservative
Very
Conservative
Extremely
Conservative
-100%
Liberal
100%
Conservative

Contributing sentiments towards policy:
65% : Layla Moran is chair of the health and social care select committee and MP for Oxford West and Abingdon "The NHS undoubtedly saved my life, but social care helps me live it."64% : Now what's needed is strong and sustained public and political support to guarantee their longevity and the buy-in from the Treasury.
61% : That is what a participant at a roundtable event on social care told me recently.
50% : There are economic costs resulting from the desperate situation in social care: unmet care needs hinder individuals from being able to work, study, acquire skills and participate in the economy, with all the resulting costs to the Treasury through the loss of potential tax receipts and the costs to the NHS if physical or mental health issues result.
*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.