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Food addiction as a causal model of obesity. Effects on stigma, blame, and perceived psychopathology
Abstract
The present study examined the impact of the food-addiction model of obesity on weight stigma directed at obese people. Participants (n = 625) were randomly assigned to four experimental conditions. They were asked to read either a food-addiction explanatory model of obesity or a nonaddiction model, and subsequently read a vignette describing a target person who met the characteristics of one of these models and was either obese or of normal weight. Questionnaires assessed participants' stigmatization and blame of targets and their attribution of psychopathology toward targets. Additional questionnaires assessed stigma and blame directed toward obese people generally, and personal fear of fat. A manipulation check revealed that the food-addiction experimental condition did significantly increase belief in the food-addiction model. Significant main effects for addiction showed that the food-addiction model produced less stigma, less blame, and lower perceived psychopathology attributed to the target described in vignettes, regardless of the target's weight. The food-addiction model also produced less blame toward obese people in general and less fear of fat. The present findings suggest that presenting obesity as an addiction does not increase weight bias and could even be helpful in reducing the widespread prejudice against obese people.
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“While the FA explanation for weight control has been shown to decrease weight stigma among groups [212,213] it has been suggested that FA can increase internalized weight stigma among individuals [214].”
From Food Addiction and Psychosocial Adversity: Biological Embedding, Contextual Factors, and Public Health Implications (2020) · cites this paper as [212]
In the citing paper
“other studies show that the FA explanation reduces weight stigma [26,27].”
From Separating the Signal from the Noise: How Psychiatric Diagnoses Can Help Discern Food Addiction from Dietary Restraint (2020) · cites this paper as [27]
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Metadata
- Authors
- Latner, J. D., Puhl, R. M., Murakami, J. M., & O'Brien, K. S.
- Affiliations
- University of Hawaiʻi at Mānoa (US)
- Yale University (US)
- Corresponding author
- Latner, Janet D.
- Venue
- Appetite
- Publisher
- Elsevier BV
- Published
- 2014-06
- Volume
- 77
- Pages
- 79-84
- ISSN
- 0195-6663 (ISSN-L)
- OpenAlex
- W1982186163
- Cited by
- 124
- References
- 42
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 4 · 2023: 6 · 2022: 10 · 2021: 7 · 2020: 47 · 2019: 4 · 2018: 3 · 2017: 7 · 2016: 10 · 2015: 8 · 2014: 8
- FWCI
- 25.67
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Appetite (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Obesity and Health Practices
- Social Sciences › Psychology › Clinical Psychology › Eating Disorders and Behaviors
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Obesity, Physical Activity, Diet
- Keywords
- food addiction, obesity, weight stigma, weight bias, blame, fear of fat, prejudice reduction, stigma reduction
- MeSH terms
- Adult, Culture, Humans, Male, Middle Aged, Obesity, Prejudice, Psychopathology, Social Perception, Stereotyping, Behavior, Addictive, Young Adult
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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