New $1.2 Million Grant Aims to Investigate the Impact of Neighborhood
- Bias Rating
-22% Somewhat Liberal
- Reliability
15% ReliableLimited
- Policy Leaning
-22% Somewhat Liberal
- Politician Portrayal
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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
-20% Negative
- Liberal
- Conservative
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Reliability Score Analysis
Policy Leaning Analysis
Politician Portrayal Analysis
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Contributing sentiments towards policy:
63% : The complexity of the methodologies being employed holds the possibility for defining new pathways for public health interventions aimed at fostering healthier communities, not just in Arizona but potentially beyond.60% : In a pioneering research initiative, Chris Lim, PhD, an assistant professor of environmental health sciences at the University of Arizona Mel and Enid Zuckerman College of Public Health, has secured a substantial grant of $1.2 million from the American Cancer Society.
48% : Iman Hakim, MD, PhD, MPH, dean of the Zuckerman College of Public Health, has noted the transformative potential of this project, stating that this comprehensive approach to investigating the relationship between neighborhood environments and cancer risk has not been studied at such a depth before.
41% : Subject of Research: Links between neighborhood environment and cancer risk Article Title: Researcher Receives $1.2 Million Grant to Explore Neighborhood Environment and Cancer Risk News Publication Date: October 2023 Web References: University of Arizona Link References: American Cancer Society Image Credits: University of Arizona Mel and Enid Zuckerman College of Public Health Keywords: Cancer risk, Environmental health, Cancer research, Food environment, Machine learning, Public health, Health disparities, Urban design, Community health, Socioeconomic status, Health equity, Neighborhood effects.
*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.