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Cited by 90Obesity and Health PracticesMedia Influence and HealthPatient-Provider Communication in Healthcare

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Impact of Genetic Causal Information on Medical Students’ Clinical Encounters with an Obese Virtual Patient: Health Promotion and Social Stigma

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Persky, Susan, Eccleston, Collette P.·2011·Annals of Behavioral Medicine
DOI 10.1007/s12160-010-9242-0PMC PMC3098938
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Abstract

BACKGROUND: Health care providers will increasingly encounter information about the genetics of obesity as genetics research progresses. PURPOSE: This study explores whether information about the genetics of obesity reduces medical student stigmatization of obese patients, and how it affects rates of health behavior-related referral. METHODS: One hundred and ten third and fourth year medical students were randomly assigned to read about genetic or behavioral mechanisms of obesity, or a control topic. Students interacted with an obese virtual patient in a virtual clinic and completed a battery of measures. RESULTS: Rates of most health behavior screening recommendations (weight loss, exercise, and diet consultations) were lower among participants exposed to genetic causal information than control. The genetic causal information group exhibited less negative stereotyping of the patient than control, F(1,105) = 5.00, p = 0.028, but did not differ in anticipated patient adherence, F(1,105) = 3.18, p = 0.077. CONCLUSIONS: Information highlighting genetic contributions to obesity may lead to both positive and negative outcomes. Communication about the genetics of obesity should discuss the multi-factorial and non-deterministic nature of genetic risk.

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  • Evidence from several countries shows that when individuals attribute the causes of obesity primarily to internal, controllable factors or personal choices, they exhibit higher weight bias, whereas acknowledging the complex causes of obesity, including genetics, biology, and environmental factors, is associated with lower levels of weight bias and less blame.

    “Evidence from several countries68–71 shows that when individuals attribute the causes of obesity primarily to internal, controllable factors or personal choices, they exhibit higher weight bias, whereas acknowledging the complex causes of obesity (including elements such as genetics, biology, and environmental factors) is associated with lower levels of weight bias and less blame.”

    From Joint international consensus statement for ending stigma of obesity (2020) · cites this paper as [68]

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Metadata

Authors
Persky, S., & Eccleston, C. P.
Affiliations
  • National Institutes of Health (US)
  • Syracuse University (US)
Corresponding author
Persky, Susan
ORCID
Persky, Susan
Venue
Annals of Behavioral Medicine
Publisher
Oxford University Press (OUP)
Published
2010-12-07
Volume
41
Issue
3
Pages
363-372
ISSN
  • 0742-5554 (ISSN-L)
  • 0883-6612
  • 1532-4796
DOI
10.1007/s12160-010-9242-0
PMC
PMC3098938
OpenAlex
W2056398292
Cited by
90
References
4
Citations by year
2026: 4 · 2025: 4 · 2024: 13 · 2023: 6 · 2022: 7 · 2021: 3 · 2020: 7 · 2019: 4 · 2018: 6 · 2017: 7 · 2016: 4 · 2015: 5 · 2014: 5 · 2013: 12 · 2012: 1
FWCI
4.76
Type
journal article
Language
en
Open access
Locations
  • Annals of Behavioral Medicine (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • National Institutes of Health (Z99 HG999999)
  • National Human Genome Research Institute
Topics
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Obesity and Health Practices
  • Social Sciences › Communication › Media Influence and Health
  • Health Sciences › Health Professions › General Health Professions › Patient-Provider Communication in Healthcare
Keywords
obesity genetics, medical students, virtual patient simulation, health promotion, social stigma, exercise referral, patient adherence, stereotyping, genetic risk communication
MeSH terms
Adult, Attitude of Health Personnel, Education, Medical, Female, Health Promotion, Humans, Male, Obesity, Random Allocation, Students, Medical, User-Computer Interface, Patient Simulation, Social Stigma

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