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It is widely recognised that weight stigma in news media is a problem, with selection of images, language, and news frames shown to contribute to negative and stereotypical representations. In response, several Australian, UK, and international organisations active in improving perceptions and treatment of people with obesity have published media guidelines. Some of these guidelines have been developed drawing on linguistic research or expertise and thus represent clear attempts at societal impact (changes in journalism practices, with presumed flow-on effects on policymaking, public perception, discourse, etc.). This chapter first reviews the recommendations that obesity media guidelines make, focusing specifically on language use. We show similarities and differences across six guidelines published for Australian, UK, and international contexts. Taking a corpus linguistic approach, we then examine to what extent selected advice on language choices from the guidelines is adhered to in journalism in Australia. We analyse dispreferred language (identity-first language and use of nominal-adjectival, the obese), preferred language (person-first language), and pejorative versus euphemistic labels. Focusing on people-first and identity-first language, we also include a comparison with a similar British corpus. The chapter concludes with critical reflections on application and impact, including the potential role of corpus linguistics in this area.
The 49-item Fat Attitudes Assessment Toolkit (FAAT; Cain et al., 2022) is a multidimensional, non-stigmatizing measure of contemporary attitudes towards fatness and fat people, some of which are targeted in stigma reduction research. The FAAT can be administered online or in-person to adults and is free to use. This chapter first discusses the development of the FAAT and then provides evidence of its psychometrics. More specifically, the FAAT has a 9-factor structure within exploratory and confirmatory factor analyses: Empathy (7 items), Activism Orientation (7 items), Size Acceptance (6 items), Attractiveness (5 items), Critical Health (5 items), General Complexity (6 items), Socioeconomic Complexity (3 items), Responsibility (6 items), and Body Acceptance (4 items). Internal consistency reliability, test-retest reliability, convergent validity, and discriminant validity support the use of the FAAT. Next, this chapter provides the FAAT items in their entirety, instructions for administration and scoring, and the item response scale. Logistics of use, such as permissions, copyright, and contact information, are available for readers.
The 11-item Weight Bias Internalization Scale (WBIS; Durso & Latner, 2008) is a self-report measure that assesses levels of internalized weight bias. The WBIS can be administered online or in-person to adolescents and adults with overweight/obesity and is free to use in any setting. This chapter discusses the development of the WBIS, and then provides evidence of its psychometric properties in a variety of samples. Specifically, the WBIS has been found to have a unitary-factor structure within exploratory and confirmatory factor analyses and is invariant across gender and weight status. Internal consistency reliability, and convergent, incremental, and discriminant validity support the use of the WBIS. Next, this chapter provides the WBIS items in their entirety, instructions for administering the WBIS to participants, the item response scale, and the scoring procedure. Information on translations of the WBIS to seven other languages are available. Logistics of use, such as permissions, copyright, and contact information, are provided for readers.
The 40-item Fat Microaggressions Scale (FMS; Lindloff et al., 2024) assesses the frequency that fat people experience weight-related microaggressions, where microaggressions are brief, recurrent, commonplace acts that demean and discriminate against members of stigmatized groups. The FMS can be administered online or in-person to adolescents and adults who self-identify as fat, overweight, or higher-weight, and is free to use. This chapter first discusses the development of the FMS and then provides evidence of its psychometrics. More specifically, the FMS has a 4-factor structure within exploratory and confirmatory factor analyses: Direct Experiences (11 items), Indirect Experiences (12 items), Benevolent Weightism (11 items), and Clothing Exclusion (6 items). Internal consistency reliability, test-retest reliability, criterion-related, incremental, and known-groups validity support the use of the FMS. Next, this chapter provides the FMS items in their entirety, instructions for administration and scoring, and the item response scale. Logistics of use, such as permissions, copyright, and contact information, are available for readers.
Practicing self-compassion – kindness towards ourselves, an understanding of our common humanity, and mindfulness – can be an important contributor to the development of a positive body image.
There are many ways to practice self-care that extend beyond grooming practices and may include nurturing our social relationships.
Examining what it is that adds meaning to our lives and working to enhance our eudaimonic well-being can also enhance our body image.
While effective treatments exist for binge-eating disorder (BED), prediction of treatment outcomes has proven difficult, and few reliable predictors have been identified. Machine learning is a promising method for improving the accuracy of difficult-to-predict outcomes. We compared the accuracy of traditional and machine-learning approaches for predicting BED treatment outcomes.
Methods
Participants were 191 adults with BED in a randomized controlled trial testing 6-month behavioral and stepped-care treatments. Outcomes, determined by independent assessors, were binge-eating (% reduction, abstinence), eating-disorder psychopathology, and weight loss (% loss, ⩾5% loss). Predictors included treatment condition, demographic information, and baseline clinical characteristics. Traditional models were logistic/linear regressions. Machine-learning models were elastic net regressions and random forests. Predictive accuracy was indicated by the area under receiver operator characteristic curve (AUC), root mean square error (RMSE), and R2. Confidence intervals were used to compare accuracy across models.
Results
Across outcomes, AUC ranged from very poor to fair (0.49–0.73) for logistic regressions, elastic nets, and random forests, with few significant differences across model types. RMSE was significantly lower for elastic nets and random forests v. linear regressions but R2 values were low (0.01–0.23).
Conclusions
Different analytic approaches revealed some predictors of key treatment outcomes, but accuracy was limited. Machine-learning models with unbiased resampling methods provided a minimal advantage over traditional models in predictive accuracy for treatment outcomes.
Research over the past fifty years has shown weight stigma to be a pervasive form of social prejudice that is found in nearly every aspect of children’s lives – in school, peer relations, the media, even their own homes – and yet weight stigma is often not recognized as a social justice issue for children. This chapter explores the prevalence and presentation of weight bias in youth and the consequences of growing up in an environment that does not recognize body diversity as a natural part of human diversity. Evidence suggests that being the target of weight-based prejudice and discrimination has serious and long-lasting consequences for children’s physical, social, and emotional health and well-being. While higher-weight children and adolescents undeniably bear the brunt of societal weight stigma, youth across the weight spectrum are negatively affected by a culture that idealizes thinness and condemns fatness. The prevalence of body image and eating concerns, weight-related teasing, and bullying among youth underscores the need for sociocultural change in values and views around body size. This chapter considers important intersections of weight, health, and social justice in youth, and concludes with a case example of structural efforts to promote body respect and equality.
The purpose of this scoping review was to explore the evidence on how perceptions and/or experiences of weight bias in primary health care influence engagement with and utilization of health care services by individuals with obesity.
Background:
Prior studies have found discrepancies in the use of health care services by individuals living with obesity; a greater body mass index has been associated with decreased health care utilization, and weight bias has been identified as a major barrier to engagement with health services.
Methods:
PubMed was searched from January 2000 to July 2017. Four reviewers independently selected 21 studies examining perceptions of weight bias and its impact on engagement with primary health care services.
Findings:
A thematic analysis was conducted on the 21 studies that were included in this scoping review. The following 10 themes were identified: contemptuous, patronizing, and disrespectful treatment, lack of training, ambivalence, attribution of all health issues to excess weight, assumptions about weight gain, barriers to health care utilization, expectation of differential health care treatment, low trust and poor communication, avoidance or delay of health services, and ‘doctor shopping’. Overall, our scoping review reveals how perceptions and/or experiences of weight bias from primary care health professionals negatively influence patient engagement with primary health care services.
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