Geographic and Longitudinal Trends in Media Framing of Obesity in the United States

Chiang, Jonathan; Arons, Abigail; Pomeranz, Jennifer L.; Siddiqi, Arjumand; Hamad, Rita

doi:10.1002/oby.22845

Cited by 8 publications

(8 citation statements)

References 38 publications

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“…As the current evidence suggests that beliefs about the causes of obesity are resistant to change, more innovative methods will be needed that can combat the repeated exposure to media, which regularly emphasises that obesity is just a symptom of poor self-control [ 27 , 28 , 29 ]. Frequently used techniques such as including images, providing evidence, and providing individual narratives have proved ineffectual in the current study and previous research [ 33 , 34 , 35 ].…”

Section: Discussionmentioning

confidence: 99%

“…This may indicate the existence of the correspondence bias for obesity. Exposure to media coverage of obesity may further entrench these beliefs as newspapers are more likely to highlight individual-level drivers for obesity, an effect that is magnified in newspapers categorised as less liberal [ 27 , 28 , 29 ]. This overestimation of the role of individual responsibility at the expense of environmental influences may explain, in part, the relatively low public support for government intervention to tackle obesity by changing environments [ 4 , 24 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

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Communicating Evidence about the Causes of Obesity and Support for Obesity Policies: Two Population-Based Survey Experiments

Reynolds

Vasiljevic

Pilling

et al. 2020

IJERPH

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Public support for numerous obesity policies is low, which is one barrier to their implementation. One reason for this low support is the tendency to ascribe obesity to failings of willpower as opposed to the environment. Correlational evidence supports this position. However, the experimental evidence is mixed. In two experimental studies, participants were randomised to receive no message, messages about the environment’s influence on obesity (Study 1 & 2), or messages about the environment’s influence on human behaviour (Study 1). We investigated whether communicating these messages changed support for obesity policies and beliefs about the causes of obesity. Participants were recruited from nationally representative samples in Great Britain (Study 1 & 2) and the USA (Study 2) (total n = 4391). Study 2 was designed to replicate existing research. Neither study found evidence that communicating the messages increased support for obesity policies or strengthened beliefs about the environment’s role in obesity. Study 2, therefore, did not replicate two earlier experimental studies. Instead, the studies reported here suggest that people’s beliefs about the causes of obesity are resistant to change in response to evidence and are, therefore, not a promising avenue to increase support for obesity policies.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Communicating Evidence about the Causes of Obesity and Support for Obesity Policies: Two Population-Based Survey Experiments

Reynolds

Vasiljevic

Pilling

et al. 2020

IJERPH

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show abstract

“… 36 In recent years, there is an increased usage of machine learning in obesity research. 37 , 38 , 39 , 40 Studies that have applied machine learning to obesity have helped predict the disease itself as well as understand the underlying biological and psychological mechanisms of this disease. In this study, machine learning was utilized to predict in‐hospital mortality in patients with severe obesity.…”

Section: Discussionmentioning

confidence: 99%

“…Big data has the potential to help us understand obesity and to address the challenges of this 21st‐century epidemic 36 . In recent years, there is an increased usage of machine learning in obesity research 37‐40 . Studies that have applied machine learning to obesity have helped predict the disease itself as well as understand the underlying biological and psychological mechanisms of this disease.…”

Section: Discussionmentioning

confidence: 99%

Machine learning to predict in‐hospital mortality among patients with severe obesity: Proof of concept study

Soffer

Zimlichman

Levin

et al. 2022

Obesity Science & Practice

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Objectives Hospitalized patients with severe obesity require adapted hospital management. The aim of this study was to evaluate a machine learning model to predict in‐hospital mortality among this population. Methods Data of unselected consecutive emergency department admissions of hospitalized patients with severe obesity (BMI ≥ 40 kg/m 2 ) was analyzed. Data was retrieved from five hospitals from the Mount Sinai health system, New York. The study time frame was between January 2011 and December 2019. Data was used to train a gradient‐boosting machine learning model to identify in‐hospital mortality. The model was trained and evaluated based on the data from four hospitals and externally validated on held‐out data from the fifth hospital. Results A total of 14,078 hospital admissions of inpatients with severe obesity were included. The in‐hospital mortality rate was 297/14,078 (2.1%). In univariate analysis, albumin (area under the curve [AUC] = 0.77), blood urea nitrogen (AUC = 0.76), acuity level (AUC = 0.73), lactate (AUC = 0.72), and chief complaint (AUC = 0.72) were the best single predictors. For Youden’s index, the model had a sensitivity of 0.77 (95% CI: 0.67–0.86) with a false positive rate of 1:9. Conclusion A machine learning model trained on clinical measures provides proof of concept performance in predicting mortality in patients with severe obesity. This implies that such models may help to adopt specific decision support tools for this population.

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“…Most studies on media framing are approached in two ways; by exploring the content of news (frame building) and examining the effect news has on the audience (frame setting) (de Vreese, 2005;Lecheler & De Vreese, 2019;Lukyanova, 2018;Thankachan & Thomas, 2021). Regarding frame building, a plethora of empirical studies have been conducted exploring the media's framing of issues such as public health (Chiang et al, 2020;Kenterelidou, 2012;Ogbodo et al,2020); politics, elections and politicians (Araujo & Prior, 2021;Lambert, 2018;Groshek & Al-Rawi, 2013); environmental issues (Bowe et al, 2014;Olsen, & Osmundsen, 2017;Weathers, & Kendall, 2016); social movements (Mendes, 2011;Sheoin, 2013) and conflict/war (Vladisacljevic & Voltmer, 2017;Atanesyan, 2020;Makhortykh & Sydorova, 2017). Other studies (Andersson, 2022;Anipah, 2017;Thuo, 2012) have also addressed generic issues.…”

Section: Introductionmentioning

confidence: 99%

Research on Media Framing in Ghana: A Systematic Review

2022

RHSS

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This systematic review interrogates the present state of framing research in Ghana. The purpose is to synthesise the existing literature on media framing in the Ghanaian context and to detect the gaps that needs to be filled. A qualitative content analysis of seventeen open-access journal articles retrieved from five databases (International Knowledge Sharing Platform, Taylor and Francis Online, Semantic Scholar, Research Gate, Sage Publications) is conducted to examine the issues discussed in the articles, the mass media type analysed and the research methods used in addressing the issues in the articles. The articles identified were published between 2013 and 2022. Content analysed data were thematically discussed to answer the research questions. The study revealed that research on media framing in Ghana centred on health and sexuality, politics and elections, environmental issues and others. The review further identified fifteen empirical studies out of the seventeen articles retrieved. Researchers also focused more on exploratory (qualitative) than explanatory (quantitative) research methods. In addition, most of the identified studies were skewed more toward print media and online media (traditional media websites) than broadcast media and other forms of online media, such as blogs. This review paper is the first of its kind; hence, will provide significant knowledge on media framing in the Ghanaian context to future researchers. It will also set the premise for other reviews to be conducted on various aspects of the concept in the Ghanaian context.

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Geographic and Longitudinal Trends in Media Framing of Obesity in the United States

Cited by 8 publications

References 38 publications

Communicating Evidence about the Causes of Obesity and Support for Obesity Policies: Two Population-Based Survey Experiments

Communicating Evidence about the Causes of Obesity and Support for Obesity Policies: Two Population-Based Survey Experiments

Machine learning to predict in‐hospital mortality among patients with severe obesity: Proof of concept study

Research on Media Framing in Ghana: A Systematic Review

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