Article | Published:

Behavior, Psychology and Sociology

Framing obesity a disease: Indirect effects of affect and controllability beliefs on weight bias

International Journal of Obesityvolume 42pages18041811 (2018) | Download Citation



Obesity has been declared a disease by the American and Canadian Medical Associations. Although these declarations sparked much debate as to the impact of framing obesity as a disease on weight bias, strong empirical research is needed to examine this impact. The current study examined the impact of framing obesity a disease on weight bias, focusing on moderating and mediating processes.


A sample of 309 participants living in the United States or Canada was recruited from Crowdflower. Participants completed measures of demographics, ideology, general attitudes, and previous contact quality and quantity with people living with obesity. Participants then read one of three articles as part of an experimental manipulation framing obesity as a disease, obesity not as a disease, and a control article unrelated to obesity. Post-manipulation included measures of affect, disgust, empathy, blame, and weight bias.


Orthogonal contrasts were used to compare the obesity-disease condition to the obesity-not-disease condition and control condition. The manipulation had a direct effect on affect (emotions), such that affect toward individuals with obesity was more positive in the obesity-disease condition than the obesity-not-disease and control condition combined. Exploration of moderating effects revealed that both the belief in a just world and weight satisfaction moderated the relationship between the obesity-disease manipulation and blame for obesity. Two models of indirect effects on weight bias were also examined, which demonstrated that the obesity-disease manipulation predicted less weight bias through more positive affect (model 1) as well as less weight bias through decreased blame among individuals high in belief in a just world (model 2).


This study further highlights the complex effects of declaring obesity a disease, uncovering a new direction for future research into the role of affect as well as indirect effects of characterising obesity a disease on weight bias.


Both the American and the Canadian Medical Associations have officially declared obesity a chronic disease [1, 2]. Although these declarations have been met with debate [3, 4], researchers have emphasized the importance of declaring obesity a disease with regard to policy, research, and delivery of care to individuals with obesity [5]. An important issue within this debate has been the potential impact of declaring obesity as a disease on the negative attitudes and stereotypes about individuals with obesity [6], also known as weight bias [7]. Weight bias is defined as including the negative attitudes and stereotypes about individuals with obesity, as well as weight-based discrimination [7]. Weight bias is a strong and pervasive stigma, held in the attitudes of both the general population as well as health care professionals [8,9,10,11,12], which significantly impacts the physical and psychological health of individuals living with obesity [13, 14]. In this paper, we seek to add to this discussion by reporting the results of an experimental investigation on the impact of framing obesity as a disease versus not a disease on weight bias.

The belief that weight is controllable and that individuals with obesity are responsible for their weight due to lifestyle factors is a dominant perspective in understanding weight bias [15]. Researchers have asserted that, by declaring obesity a disease, the perception of obesity as being controllable and the personal responsibility of the individual may shift, therefore reducing weight bias [6]. However, others have argued that this declaration will shift focus away from environmental factors of obesity and will therefore increase weight bias [4, 6]. This uncertainty has also been documented in the views of the general public, with one study identifying 37% of participants believing declaring obesity a disease will decrease weight bias and 31.6% of participants believing it will increase weight bias [6].

Only one recent paper has examined the impact of declaring obesity a disease on weight bias, in three experimental studies [16]. Participants were presented with a framing of obesity as follows: (1) a disease or as changeable, or (2) a disease vs. not a disease. They found framing obesity as a disease decreased stigma indirectly through reduced blameworthiness, yet also increased stigma through heightened beliefs that weight is unchangeable, which they labelled the stigma asymmetry model. Thus, characterizing obesity as a disease in order to reduce perceived controllability, and blameworthiness, of weight, may not be effective, as this reduction simultaneously heightens essentialism: perceiving that obesity is an enduring component of identity and, if obesity is bad, so too is the person.

If framing obesity as a disease is unlikely to straightforwardly reduce weight bias, it is important to include a control group as a more robust design and to consider likely moderating factors, including possible moderators of mediational processes. Hence, we conceptually replicated the Hoyt et al. [16] experimental manipulation of describing obesity as a disease or not a disease, but also included a control group and examined potential moderators. We hypothesize, as Hoyt and colleagues found, that framing obesity as a disease would predict lower explicit weight bias by reducing perceptions that people with obesity are to blame for their body weight.

As potential moderators, we assessed right-wing authoritarianism (the degree to which people support deference to authority), social dominance orientation (the degree to which people support hierarchy between groups), and just world beliefs (the degree of agreement with the belief that people get what they deserve). Higher standing on each of these is linked to more weight bias [17,18,19]. As a higher score on any of these is associated with more perceived controllability and blameworthiness for bad outcomes [20,21,22], we expected that any weight bias-reducing effects of calling obesity a disease would be more apparent for people scoring low on these dimensions.

Contact was also examined as a moderator. Contact with people with obesity, especially positive contact, is associated with lower weight bias [23, 24]. We expected that weight bias-reducing effects of the obesity as disease manipulation would be most apparent for people with more contact, those most open to positive attitudes toward people with obesity. Finally, we included several additional exploratory moderators: gender, BMI, body weight/shape satisfaction, and attitudes about obesity. These were exploratory given that more than one moderation pattern seemed possible.

This project further extends the work of Hoyt and colleagues in two additional ways. First, we thought it important to assess directly how thinking about obesity as a disease might influence how people feel about people with obesity, given that affect toward a group is often a powerful predictor of behavior toward members of that group [25]. Hence, the emotions people feel when they think of people with obesity, as well as disgust and empathy were assessed. Second, our description of obesity as a disease provides a more exact test of the hypothesis because it does not include the pro and con arguments that were embedded in the Hoyt et al. manipulation [16]. This is important because we felt it is important to investigate, absent the opinions of professionals, how people unfamiliar with the issue would respond to the idea of obesity as a disease. In addition, we included a third unrelated disease (HIV/AIDS) control condition designed to assess if our manipulation impacted reactions to people with obesity or reactions to people with health challenges generally. This condition also made it possible for us to make direction of effect comparisons. We did not want to preclude the possibility that both calling obesity a disease and declaring it not a disease might have effects that would not be discernable without a control group (comparison disease condition unrelated to obesity).

Materials and methods


Crowdflower contributors living in Canada or the United States completed a 25-min questionnaire for $5 compensation. Crowdflower is a crowdsourcing website similar to Amazon Mechanical Turk [26]. Those who closed the questionnaire before completing all measures (N = 29) and those who failed the manipulation check (N = 87, see below) were excluded, leaving a final sample of 309 participants (52.1% women, Mage = 33.17, SD = 12.24). Participants self-identified their ethnic backgrounds as White (75.4%), Asian (6.1%), Hispanic/Latin American (5.2%), Bi-racial (4.2%), African American (3.6%), American Indian/First Nations (2.3%), or other (3.2%). The majority of participants in the sample had a post-secondary education (81.2%), with the remaining participants having a high school diploma (15.9%) or some high school education (2.8%). An a-priori power analysis [27] had determined a sample size of approximately 300 would be required to detect small-to-medium effects. This study was approved by the University of Calgary Research Ethics Board. Informed consent was obtained from all participants prior to their participation in the study.


Pre-manipulation measures


Participants completed questions on gender, age, race/ethnicity, occupation, and satisfaction with body weight and shape. Participants also self-reported their height and weight, which was used to calculate body mass index (BMI) (MBMI 26.09 kg/m2, SD = 9.61 kg/m2).

Social attitudes and ideology

We included the 13-item Right-Wing Authoritarianism scale (α = .84; 1 [strongly disagree] to 9 [strongly agree]) [28], the 4-Item Social Dominance Orientation scale (α = .80; 1 [Extremely Oppose] to 10 [Extremely Favor]) [29], and the 6-Item Global Belief in a Just World Scale (α = .82; 1 [strongly disagree] to 6 [strongly agree]) [30]. Items were averaged after reverse-scoring necessary items with higher scores indicating higher levels of the given construct.


Evaluations of people with obesity were tapped with an attitude thermometer [31], divided into 10° range increments, ranging from 0–10° (extremely unfavorable) to 91–100° (extremely favorable).

Quantity and quality of contact

Participants completed a 5-item quantity of contact and (α = .89; 1 [none at all] to 7 [a great deal]) and 5-item quality of contact (α = .92; 1 [none at all] to 7 [a great deal])) of contact scale [32].


Following the above measures and prior to the manipulation measures, participants read “We would now like you to read a recent news article and then we will ask you some questions about it.” Participants were then randomly assigned (using Qualtrics software) to read one of three articles created for the study: an article about obesity stating that obesity is a disease (condition 1), an article about obesity stating that obesity is not a disease (condition 2), or an article about HIV (control condition). See supplemental material for exact wording of articles.

Post-manipulation measures

Attention/Manipulation check

As an attention check, in the obesity conditions participants were asked (yes/no) whether obesity was described as a disease in the article they read. In the HIV article (control condition), participants were asked (yes/no) whether HIV was described as a disease in the article. Participants were also asked post-manipulation whether people with obesity have a medical condition that is a disease (1 [strongly disagree] to 9 [strongly agree]). This was employed as a manipulation check.


Participants indicated the extent to which they experience positive (warm, happy, reverse scored) and negative (resentment, fear, disgust, pity, guilt) feelings towards individuals with obesity (α = .68, 1 [Not At All] to 9 [Extremely]). The negative affect items were previously established by Cottrell and Neuberg [25] in an affective reactions scale; the positive items were added by the researchers. To improve reliability, one item, “When I think about obese people I experience happy feelings” was removed. Items were averaged after with higher scores indicating more negative affect.


Disgust towards people with obesity was measured using a 5-item scale measuring negative affective reactions towards persons with obesity (α = .84; 1 [strong disagree] to 7 [strongly agree]) [33].


Participants indicated the extent to which they had empathy for people with obesity on a 6-item scale (α = .94; 1 [not at all] to 7 [very much]) [34].


The extent to which people with obesity are to blame for their weight was measured using 4 items based on Weiner’s analysis of blame attribution [35]. These items tapped perceptions of the extent to which people with obesity: 1) have obesity because of their own actions, 2) are responsible for their body size, 3) are to blame for their body size, and 4) can control their body size (α = .87). Items were averaged with higher scores indicating greater blame.

Weight bias

Explicit weight bias assessing the attitudes, stereotypes, and discriminatory behaviour towards individuals with obesity was measured using the 20-item Universal Measure of Bias Fat version (UMB-Fat; α = .68; 1 [strongly disagree] to 7 [strongly agree]) [36]. Items were averaged after reverse-scoring necessary items with higher scores indicating greater weight bias.

Public health threat perception

A four-item measure was created by the researchers assessing for the extent to which participants believed obesity is a threat to the public health care system (α = .73; 1 [strongly agree] to 5 [strongly disagree]).

Statistical analysis

Given that our focus was on the impact of declaring obesity a disease, our primary interest was in examining the obesity-disease condition relative to the other two conditions combined. Orthogonal contrasts were employed to represent the manipulation. Orthogonal contrasts assign numerical weights to compare conditions or groups of conditions with others [37]. Contrast 1 (C1) compared obesity-disease to the obesity-not-disease and the HIV conditions (−2/3, 1/3, 1/3), and contrast 2 (C2) compared obesity-not-disease to the HIV condition (0, −1/2, 1/2). Contrast 2 was included to maintain orthogonality.

Upon removing participants who did not answer attention questions correctly (see below), we first tested whether the obesity-disease manipulation directly influenced any of the post-manipulation dependent variables (affect, disgust, empathy, blame, weight bias, or public health threat perceptions). Next, we examined whether the influence of the obesity-disease manipulation on any of the post-manipulation dependent variables was moderated by any of the potential moderator variables (right-wing authoritarianism, social dominance orientation, just world beliefs, pre-manipulation attitudes, quantity of contact, quality of contact, BMI, body weight satisfaction, body shape satisfaction, or gender). That is, we tested whether the influence of the obesity-disease manipulation on any of the dependent variables was conditional upon any of the moderators. Finally, given our interest in examining the influence of the obesity-disease manipulation on weight bias, we formulated and tested mediation models, whereby the obesity-disease manipulation indirectly influences weight bias.


Attention/manipulation check

Participants were considered to have “passed” the attention check if they (1) responded “yes” to whether obesity was described as a disease in the article in the obesity-disease condition (120/131), (2) responded “no” to whether obesity was described as a disease in the article in the obesity-not-disease condition (76/131), or (3) responded “yes” to the question of whether HIV was described as a disease in the article in the HIV condition (113/134).Footnote 1

Participants in the obesity-disease condition (M= 5.77, SD = 2.19) reported obesity as a disease to a greater extent than participants in the obesity-not-disease (M= 4.61, SD = 2.06, t [194] = 3.70, p < .001) or HIV conditions (M= 5.10, SD = 2.16, t [231] = 2.35, p= .020), F (2, 306) = 7.19, p = .001. Participants in the obesity-not-disease and HIV conditions did not differ on this item, t (187) = -1.57, p = .119).

In ancillary analyses, describing obesity as a disease accounted for more variance in emotional reactions than perceived blameworthiness (16.4 vs. 7.1%). In addition, emotional reactions to people with obesity accounted for more variance in weight bias than blameworthiness (38.7 vs. 25.1%).

Direct effects of the obesity-disease manipulation

Each post-manipulation variable described above (with the exception of the attention/manipulation check) was regressed onto C1 and C2. The obesity-disease manipulation had a direct effect on one post-manipulation criterion variable: affect. C1 significantly predicted affect such that participants’ affect toward people with obesity was more positive in the obesity-disease condition than the not-disease and HIV conditions combined (b = .46, 95% CI: 0.15, 0.78). C2 was not significant for affect (or any other variable).

Testing potential moderators

For each potential moderator, each post-manipulation criterion variable was regressed on C1, C2, and the moderator on Step 1, and the interactions between C1 and the moderator on Step 2. Any significant interactions were probed using the PROCESS macro for SPSS [38] such that conditional effects of the manipulation on the criterion variable were examined at low (1 SD below), medium (mean) and high (1 SD above) values of the moderator.

Moderated effects were present for one post-manipulation criterion variable: blame. The relationship between the manipulation and blame was moderated by belief in a just world (b = 0.48, 95% CI: 0.07, 0.89). Characterizing obesity as a disease reduced blameworthiness, but only for participants with strong just world beliefs (b = 0.66, 95% CI: 0.13, 1.18). Weight satisfaction (b = 0.31, 95% CI: 0.07, .54) also moderated blame such that lower perceptions of blame in the obesity is a disease condition was evident for those high in weight satisfaction (b = 0.71, 95% CI: 0.20, 1.23).

Testing exploratory mediation models

Although the obesity-disease manipulation did not directly influence weight bias, variables that were impacted by the manipulation, either directly or indirectly, did influence weight bias. Specifically, more positive affect associated with people with obesity (r (308) = 0.43, p < 0.001) and less blame for obesity (r (308) = 0.32, p < 0.001) were each associated with less weight bias. Thus, we formulated and tested two models. First, we tested a mediation model whereby the obesity-disease manipulation influenced weight bias indirectly through more positive affect. Second, we tested a moderated mediation model whereby the obesity-disease manipulation influenced weight bias indirectly through less blame, with just world beliefs operating as a moderator. Models were tested using the PROCESS macro for SPSS [38].

Consistent with results described above, the first model demonstrated that the obesity-disease manipulation predicted more positive affect (b= −0.16, 95% CI: −0.26, −0.05), which went on to predict less weight bias (b= 0.20, 95% CI: 0.16, 0.26). Critically, an indirect effect of the obesity-disease manipulation on weight bias through affect (b= −0.03, 95% CI = −0.05, −0.01) was revealed. Testing the second model again showed no influence of the obesity-disease manipulation on blame and a relationship between less blame and less bias (b= 0.12, 95% CI: 0.08, 0.l7). The obesity-disease manipulation did, however, predict weight bias indirectly through less blame for those highest (but not low or medium) in belief in a just world (b= −0.03, 95% CI = −0.06, −0.01). Figure 1 displays these results. Thus, the obesity-disease manipulation indirectly influenced weight bias.

Fig. 1
Fig. 1

Mediation and moderated mediation models. Unstandardized effects shown. **p < 0.01, ***p < 0.001. In the figure, higher scores on affect represent more negative affect; higher scores on blame represent more blame. BJW belief in a just world


The findings of our investigation highlight the complex and nuanced effects of calling obesity a disease on attitudes towards people with obesity. As with the findings of Hoyt and colleagues [16], we saw no evidence of a direct effect of our obesity as a disease/not a disease manipulation on weight bias. However, our finding that the manipulation had a direct effect on our participants’ affect about people with obesity is noteworthy and suggests another important dimension of the obesity as a disease discussion that merits further consideration. Affect (or emotions) versus weight bias (i.e., negative evaluations of those with obesity) may be the more proximal factor in how people interact with people with obesity. The possibility of this being the case “notwithstanding” one’s general attitude about people with obesity merits further investigation in studies that include opportunities to measure behaviours, including measured social interaction. As Hoyt and colleagues [16] and Haslam and Kvaale [39] have pointed out, promoting genetic explanations of a stigmatized person’s condition has the potential to exacerbate negative reactions toward them. Our finding that framing obesity as a disease—which may suggest a genetic explanation for obesity—influences how people feel about people with obesity invites an alternative more optimistic scenario. Potentially, increased positive affect about people with obesity may lower a barrier to contact with them, allowing for a more sustainable trajectory for changing attitudes and behaviours [40, 41]. Future research should examine what factors increase positive affect towards people with obesity and investigate if increasing positive affect decreases weight bias over the short and long term.

Our findings also paint a more complex picture of factors that mediate the relationship between calling obesity a disease and weight bias. Characterizing obesity as a disease was associated with increased positive affect toward people with obesity, which in turn predicted less weight bias. This again highlights the importance of thinking about the antecedents of weight bias more broadly. To date, the focus on understanding the causes of weight bias has largely been on beliefs about weight controllability, blame, and essentialism, which have been linked to cultural beliefs and ideology [15]. Prejudices are also informed by the emotions people experience in association with the target of prejudice [25]. Certainly, strongly held prejudices (i.e., labeling, stereotypes) are constituted of both negative beliefs and negative affect. However, in our study, participants in the obesity as a disease condition had more positive affect toward people with obesity, which predicted lowered bias. Thus, there is reason to believe that characterizing obesity as a disease can reduce weight bias, less by directly changing generally held beliefs, but instead by changing emotional associations with people with obesity.

Adding emotional reactions to our understanding of weight bias requires consideration of the role of blame and controllability from a new perspective. As noted in the results, characterising obesity as a disease accounted for more variance in the affect, or emotional responses, of participants than perceived blameworthiness. Moreover, unlike Hoyt et al [16], we did not find evidence of blameworthiness serving as a mediator of an effect of describing obesity as a disease on weight bias for the overall sample. However, people with strong just world beliefs were responsive to the manipulation’s effect on blame and in blame’s mediational role in the indirect effect of the manipulation on weight bias. The pattern was also unanticipated: framing obesity as a disease reduced strong just world believers’ blame perceptions, which in turn predicted reduced weight bias. These intriguing findings are suggestive of the role of the justice motive in people’s thinking about blame for obesity. People with strong just world beliefs are invested in the idea that people get what they deserve, and attributing blame is a hallmark of how they maintain that belief [42].

To find that participants with the strongest just world beliefs were the ones most affected by thinking about obesity as a disease is thus remarkable, particularly when it involves effects on perceived blame. If obesity is a disease, it becomes difficult for those needing to assign blame to people with obesity for their condition to do so. These findings need further investigation but may indicate that people with strong just world beliefs in this context might be particularly sensitive to information relevant to the viability of blame attributions. This is also consistent with research demonstrating that some prejudice reduction strategies work best for those who are highest in prejudice [43], in other words, those with the most prejudice to reduce.

Our research has some limitations. For example, the disproportionate failure of the attention check in the obesity is not a disease condition is problematic. We speculate that participants may have failed the attention check due to the subtle nature of our manipulation characterizing obesity as a disease and not a disease. This warrants further examination to ensure that no other unassessed confounding factor played a role. It is also important to note that the strategy of examining exhaustively for moderation effects resulted in a large number inferential tests; enough so that the Type I error rate is inflated. We chose not to invoke experiment-wise corrections in the spirit of learning as much as we could from this initial exploratory investigation. Finally, the impact of the obesity as a disease manipulation did not extend to a measure of disgust feelings associated with people with obesity or a measure of empathy for them. This suggests that simply characterizing obesity as a disease may have desirable emotional effects but that the effects are not sufficiently robust to influence people who are actually disgusted by obesity or to increase people’s empathy towards those with obesity. Thus, it is likely the case that the changes in affect that arise from characterizing obesity as a disease may need to be supplemented with additional emotionally impactful information and experiences, such as increased positive contact and increased empathy with people with obesity [44].

Summary and implications

In summary, this investigation highlights how characterizing obesity as a disease has implications that are neither as simple or as uniformly salutary as advocates might have assumed. This investigation complicates our understanding of the impact of framing obesity a disease on weight bias by not reproducing the blame mediational pattern Hoyt et al found [16]. At the same time, our results add a dimension and path for seeing how the disease framing of obesity decreases weight bias indirectly through increases in positive affect associated with people living with obesity. Future weight bias reduction interventions should consider mentioning that obesity is a disease given our findings suggest that this declaration has an indirect effect on reducing weight bias through increasing positive affect. Future research should also examine effective ways to increase positive affect towards people with obesity to also help reduce weight bias.


  1. 1.

    A disproportionate number of participants in the obesity-not-disease condition failed the attention check. These participants may have misremembered the article as stating obesity as a disease given the other health-relevant information present or may have misread the article. No differences were found between groups on gender, BMI, age, highest level of education, or satisfaction with body weight or shape


  1. 1.

    Rich P. CMA recognizes obesity as a disease. 2015. Accessed on 02 March 2016.

  2. 2.

    Frellick M. AMA declares obesity a disease. 2013. Accessed on 02 March 2016.

  3. 3.

    Kabat G. Why labeling obesity as a disease is a big mistake. 2013. Accessed on 02 March 2016.

  4. 4.

    Tanner M. Obesity is not a disease. 2013. Accessed on 02 March 2016.

  5. 5.

    Kahan S, Zvenyach T. Obesity as a disease: current policies and implications for the future. Curr Obes Rep. 2016;5:291–7.

  6. 6.

    Puhl RM, Liu S. A national survey of public views about the classification of obesity as a disease. Obesity (Silver Spring). 2015;23:1288–95.

  7. 7.

    Carels RA, Latner J. Weight stigma and eating behaviors. An introduction to the special issue. Appetite. 2016;102:1–2.

  8. 8.

    Andreyeva T, Puhl RM, Brownell KD. Changes in perceived weight discrimination among Americans, 1995-1996 through 2004-2006. Obesity (Silver Spring). 2008;16:1129–34.

  9. 9.

    Sabin JA, Marini M, Nosek BA. Implicit and explicit anti-fat bias among a large sample of medical doctors by BMI, race/ethnicity and gender. PLoS ONE. 2012;7:e48448.

  10. 10.

    Poon MY, Tarrant M. Obesity: attitudes of undergraduate student nurses and registered nurses. J Clin Nurs. 2009;18:2355–65.

  11. 11.

    Diversi TM, Hughes R, Burke KJ. The prevalence and practice impact of weight bias amongst Australian dietitians. Obes Sci Prac. 2016;2:456–65.

  12. 12.

    Cavaleri R, Short T, Karunaratne S, Chipchase LS. Weight stigmatization in physiotherapy: a systematic review. Phys Ther Rev. 2016;21:1–9.

  13. 13.

    Stevens SD, Herbozo S, Morrell HE, Schaefer LM, Thompson JK. Adult and childhood weight influence body image and depression through weight stigmatization. J Health Psychol. 2017;22:1084–93.

  14. 14.

    Sutin AR,Stephan Y,Terracciano A, Weight discrimination and risk of mortality. Psychol Sci. 2015;26:1803–11.

  15. 15.

    Crandall R, Reser A. Attributions and weight-based prejudice. In: Brownell K, Puhl R, Schwartz M, Rudd L, editors. Weight bias: Nature, consequences and remedies. New York: Guildford Press; 2005.

  16. 16.

    Hoyt CL, Burnette JL, Auster-Gussman L, Blodorn A, Major B. The obesity stigma asymmetry model: The indirect and divergent effects of blame and changeability beliefs on antifat prejudice. Stigma & Health. 2016;2:53–65.

  17. 17.

    Ebneter DS, Latner JD, O’Brien KS. Just-world beliefs, causal beliefs, and acquaintance: Associations with stigma toward eating disorders and obesity. Person Individ Differ. 2011;51:618–22.

  18. 18.

    Elison ZM, Çiftçi A. Digesting antifat attitudes: Locus of control and social dominance orientation. Transl Iss Psychol Sci. 2015;1:262–70.

  19. 19.

    O’Brien KS, Latner JD, Ebneter D, Hunter JA. Obesity discrimination: the role of physical appearance, personal ideology, and anti-fat prejudice. Int J Obes (Lond). 2013;37:455–60.

  20. 20.

    Jackson LE, Gaertner L. Mechanisms of moral disengagement and their differential use by right‐wing authoritarianism and social dominance orientation in support of war. Aggress Behav. 2010;36:238–50.

  21. 21.

    Hafer CL, Sutton R. Belief in a just world. In: CSMe Sabbagh, editor. Handbook of social justice theory and research. New York, NY, USA: Springer; 2016. p. 145–60.

  22. 22.

    Magallares A. Right wing autoritharism, social dominance orientation, controllability of the weight and their relationship with antifat attitudes. Univ Psychol. 2014;13:771–9.

  23. 23.

    Koball AM, Carels RA. Intergroup contact and weight bias reduction. Transl Iss Psychol Sci. 2015;1:298–306.

  24. 24.

    Kushner RF, Zeiss DM, Feinglass JM, Yelen M. An obesity educational intervention for medical students addressing weight bias and communication skills using standardized patients. BMC Med Educ. 2014;14:53.

  25. 25.

    Cottrell CA, Neuberg SL. Different emotional reactions to different groups: a sociofunctional threat-based approach to “prejudice”. J Pers Soc Psychol. 2005;88:770–89.

  26. 26.

    Buhrmester M, Kwang T, Gosling SD. Amazon’s mechanical turk: a new source of inexpensive, yet high quality, data? Perspect Psychol Sci. 2011;6:3–5.

  27. 27.

    Faul F, Erdfelder E, Lang AG, Buchner A. G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods. 2007;39:175–91.

  28. 28.

    Funke F. The dimensionality of right-wing authoritarianism: Lessons from the dilemma between theory and measurement. Polit Psychol. 2005;26:195–218.

  29. 29.

    Pratto F, Cidam A, Stewart AL, Zeineddine FB, Aranda M, Aiello A, et al. Social dominance in context and in individuals: Contextual moderation of robust effects of social dominance orientation in 15 languages and 20 countries. Soc Psychol Personal Sci. 2013;4:587–99.

  30. 30.

    Dalbert C, Montada L, Schmitt M. Glaube an eine gerechte Welt als Motiv: validierungskorrelate zweier Skalen [Belief in a just world: validation correlates of two scales]. Psychol Beitrage. 1987;29:596–615.

  31. 31.

    MacInnis CC, Hodson G. Intergroup bias towards “group x”: Evidence of prejudice, dehumanization, avoidance, and discrimination against asexuals. Group Proc Inter Relat. 2012;15:725–43.

  32. 32.

    Islam MR, Hewstone M. Dimensions of contact as predictors of intergroup anxiety, perceived outgroup variability, and outgroup attitude: An integrative model. Personal Social Psychol Bull. 1993;19:700–10.

  33. 33.

    Brochu P. Weight prejudice and medical policy: support for an ambiguously discriminatory policy is influenced by prejudice-colored glasses. Anal Soc Iss Public Policy. 2009;9:117–33.

  34. 34.

    Batson CD, Polycarpou MP, Harmon-Jones E, Imhoff HJ, Mitchener EC, Bednar LL, et al. Empathy and attitudes: can feeling for a member of a stigmatized group improve feelings toward the group? J Pers Soc Psychol. 1997;72:105–18.

  35. 35.

    Weiner B. Judgments of responsibility: a foundation for a theory of social conduct.. New York, NY, USA: Guilford; 1995.

  36. 36.

    Latner JD, O’Brien KS, Durso LE, Brinkman LA, MacDonald T. Weighing obesity stigma: the relative strength of different forms of bias. Int J Obes (Lond). 2008;32:1145–52.

  37. 37.

    Rosnow RL, Rosenthal R, Rubin DB. Contrasts and correlations in effect-size estimation. Psychol Sci. 2000;11:446–53.

  38. 38.

    Hayes AF. An introduction to mediation, moderation, and conditional process analysis: a regression-based approach. New York, NY, USA: Guilford; 2013.

  39. 39.

    Haslam N, Kvaale EP. Biogenetic explanations of mental disorder: the mixed blessings model. Curr Dir Psychol Sci. 2015;24:399–404.

  40. 40.

    Tropp LR, Pettigrew TF. Differential relationships between intergroup contact and affective and cognitive dimensions of prejudice. Pers Soc Psychol Bull. 2005;31:1145–58.

  41. 41.

    Pettigrew TF, Tropp LR. A meta-analytic test of intergroup contact theory. J Pers Soc Psychol. 2006;90:751–83.

  42. 42.

    Ellard JH, Harvey A, Callan MJ. The justice motive: history, theory, and research. ICSMS, editor. Handbook of social justice theory and research. New York, NY, USA: Springer-Verlag; 2016. p. 127–43.

  43. 43.

    Hodson G, Costello K, MacInnis CC. Is intergroup contact beneficial among intolerant people? Exploring individual differences in the benefits of contact on attitudes. In Hodson G, Hewstone M, editors. Advances in intergroup contact. New York, NY, US: Psychology Press; 2013. p. 49–80.

  44. 44.

    Alberga AS, Russell-Mayhew S, von Ranson KM, McLaren L, Ramos Salas X, Sharma AM. Future research in weight bias: What next? Obesity (Silver Spring). 2016;24:1207–9.

Download references


SN is currently funded by a Doctoral Research Award from the Social Sciences and Humanities Research Council. ASA was previously funded by a Banting Postdoctoral Fellowship Award from the Canadian Institutes of Health Research at the University of Calgary and is currently supported by a Research Scholar Junior 1 Award from les Fonds de Recherche du Québec- Santé at Concordia University, Montreal. This research was funded by a University of Calgary Research Grants Council Grant.

Author information


  1. Werklund School of Education, University of Calgary, Calgary, Canada

    • Sarah Nutter
    •  & Shelly Russell-Mayhew
  2. Department of Exercise Science, Concordia University, Montreal, Canada

    • Angela S. Alberga
  3. Department of Psychology, University of Calgary, Calgary, Canada

    • Cara MacInnis
    •  & John H. Ellard


  1. Search for Sarah Nutter in:

  2. Search for Angela S. Alberga in:

  3. Search for Cara MacInnis in:

  4. Search for John H. Ellard in:

  5. Search for Shelly Russell-Mayhew in:

Conflict of interest

The authors declare that they have no conflict of interest.

Corresponding author

Correspondence to Shelly Russell-Mayhew.

Electronic supplementary material

About this article

Publication history