Method

Interviews were chosen as the primary instrument to investigate deeper into the system’s main actor – fat patients. They are the main stakeholder this project aims to serve, so it was essential to give them the opportunity to speak about their experiences. Interviews allow more freedom to learn from people and give them the reins to shape the direction of the process (Sanders & Stapper, 2012, p. 68). This was an important characteristic required for this project; therefore a semi-structured interview format was followed with guiding questions from the researcher. This structure allowed the participants to relay as much information about their experience as they wanted, but with specific questions that helped them stay on topic.

Interview questions were divided into three main sections. The first section contained demographic questions inquiring the age range and gender of the interviewees. This section also determined whether the participant had been classified as overweight by their healthcare provider. The second section consisted of questions regarding the participant’s experience with weight bias and how they felt or coped during these encounters. The last section questioned about weight bias management and consequences. It inquired about the interviewee’s willingness to access healthcare and any strategies they use when doing so. Ultimately, the main goal of the interviews was to get a better understanding of the occurrence of weight bias in Ontario healthcare and how it has affected fat patients.

Experience

 Recruitment for the interviews was carried out on social media platforms, specifically through Instagram and Facebook posts and stories. The criteria for participants were that they had to self-identify as fat or be medically classified as overweight. They also had to have accessed healthcare in Ontario. The first level of recruitment came in the form of opportunistic sampling. Followers with personal connections to the researcher volunteered to participate. The second level of recruitment, where most participants were gathered, was done through snowball sampling. Followers of the researcher’s social media accounts shared the recruitment materials on their own accounts. (Sanders & Stapper, 2012, p.153-154). Recruitment was carried out over three weeks, simultaneously with the interviews taking place. At the end of the research period, a total of 13 interviews were conducted. Interviews generally lasted an average of 30 to 35 minutes. All participants answered all the questions and even shared extra information on their own accord. They were passionate about describing their experiences, and every participant wanted a change in the system.

Findings

Out of the 13 participants interviewed, 12 were female, and one was gender-fluid. Participant ages ranged from 26 to 58, with more than half of them being in their 30s. All but one are currently classified as overweight by their healthcare provider. The average number of years these patients have been classified as overweight is 22 years.

Some of the critical findings discovered after analyzing the responses about weight bias experiences are:

• Ten out of the 13 participants had faced most of their weight bias from their current or previous family doctors.
• Participants primarily faced weight bias during preventive care and physicals.
• Participants have had their injuries dismissed, with their doctors resorting to weight as the cause.
• For eight out of the 13 participants, the principal diagnosis for their ailment was weight, and the main treatment was weight loss. However, each of the eight had a different issue they went in with.
• Participants were denied referrals to further tests and specialists.
• Participants have been coerced into following dieting advice and plans.
• Ten out of the 13 participants could not maintain weight loss prescribed by the doctor and gained more weight plus developed mental health complications and eating disorders.
• The participants’ biggest frustration was that even if they followed healthy lifestyles such as eating nutritious food or exercising daily, their doctors did not believe them.
• There was an extensive list of physical barriers shared: small gowns, small chairs, small blood pressure cuffs, judgemental messaging, public weighing scales and unstable beds.

Some of the key findings discovered after analyzing the responses about weight bias effects and management are:

• Eight out of the 13 total participants said they are now reluctant to access healthcare, especially preventive care.
• Less than half said they still access preventive care but prepare intensively before going. They mentally get themselves ready to stand up for themselves, do their own research and read up on Health at Every Size affirmations. Much effort is made.
• Some other tactics they commonly use are being persistent about getting diagnoses beyond weight, refusing to get weighed or discussing weight, and actively looking for fat-positive providers.

Interviews also brought up some unique, absorbing insights from some of the participants. One participant mentioned that they have not faced as much weight bias at appointments during the ongoing pandemic simply because they were over the phone. Another participant mentioned that it is not just medical professionals who show judgment but also office staff at clinics such as receptionists and administrators.

Some pointed out that they do not understand why doctors think fat people are not aware of their weight. These people live in their bodies every day and understand their weight and their abilities. Chances are they have also tried numerous weight loss plans in their lifetime.

Another insight shared was that when fat patients have positive healthcare experiences, it feels like a rare moment of celebration and triumph, which is problematic, considering it is an essential service. One participant also mentioned that what frustrated them the most is the lack of dignity they experience when they are continually being told to lose weight as if they are a little child being told to obey rules.

In conclusion, the interviews painted a grim picture of the realities of weight bias in healthcare currently in this province.

The second primary research method used was a survey to measure the level of weight bias healthcare providers in Ontario held. The survey was designed using three pre-existing scales: Beliefs About Obese People, Attitude Towards Obese People and Fatphobia Scale. These scales are part of the bias toolkit created by the Rudd Centre for Food Policy and Obesity (The Rudd Centre For Food Policy & Obesity, n.d.).

Beliefs About Obese People (BAOP) is an 8 – statement scale that measures belief about the underlying reasons for obesity. Items are scored on a 6-point Likert scale (strongly disagree to strongly agree). Higher scores indicate the belief that genetic and environmental causes drive obesity, and lower scores indicate that obesity is caused by a lack of personal control (Poustchi et al, 2013).

Attitudes Towards Obese People (ATOP) is a 20 – statement scale that measures perceptions regarding obese people. Items are also scored on a 6-point Likert scale (strongly disagree to strongly agree). Higher scores indicate more positive attitudes, and lower scores indicate more negative attitudes (Poustchi et al, 2013).

Fat Phobia Scale (FPS) is a 14 – item scale that requires participants to indicate which adjective better describes obese people on a 5-point scale, e.g. active to inactive. Higher scores indicate high levels of fatphobia, thus more negative stereotypes (Poustchi et al, 2013).

The survey had three sections with each of these scales. ATOP and FPS scales were reduced to 10 items only to ensure the survey was not too long. Survey data was analyzed using the instructions provided by each scale.

Causal Layered Analysis for the Issue of Weight Bias in Healthcare

Worldview

These ingrained societal beliefs enforce the systemic causes that encourage weight bias. Diving into literature and stories surrounding the systemic causes revealed these worldviews. Primary data from the interviews also reflected some of these sentiments. Observation into social media comments on the literature pieces also disclosed these perceptions in people.

• All fat people lead unhealthy lifestyles, and being fat is one of the worst things. Making anything fat inclusive encourages poor health habits (Cernik, 2018).
• Healthy habits and being thin makes you morally superior. Being fat is a sign of failure because you eat poorly and stay on the couch (Your Fat Friend: 2020).
• Fat people are an economic burden (Tremmel et al, 2017).
• Doctors know our bodies best. They have gone through rigorous education, so they are always right.

Myth/Metaphor:

Unconscious beliefs that drive the worldviews listed above. These beliefs were extrapolated from both interview data and secondary research findings (which revealed worldviews) and rewritten to summarize overall feelings.

• Fat is evil.
• Obesity kills.
• Doctors are angels in white coats (A metaphor often used to describe doctors).

Fill in the blank to show common myths and misunderstandings about obesity.

Experience

Survey recruitment was carried out through opportunistic and snowball sampling via Instagram, Facebook, as well as LinkedIn. The participant criteria were that they had to be a healthcare professional or student in Ontario. This included general physicians, specialists, nurses, technicians, physiotherapists, dieticians, mental health professionals and paramedics. Acquiring survey participants was a bit more frustrating because the response was not as big. Recruitment was carried out over a full month. In the end, ten participants completed the survey.

Limitations

With such a low number of participants, saturation was not reached, and the survey data was not correctly representative. For future improvements, creating a shorter survey and allowing for a more extended recruitment period could help get more responses. Direct recruitment could also provide more fruitful results.

Findings

In total, 60% of the respondents had positive attitudes towards obese people (ATOP), and 60% believe that obesity is under the obese individual’s personal control (BAOP).

All the respondents scored above-average on the Fatphobia Scale, indicating the presence of fatphobia. Despite the low numbers and lack of representative data, some compelling anecdotal findings were discovered:

Respondents who had more negative attitudes towards obese people believed obesity was in personal control of the individual. However, at the same time, half of the people who had positive attitudes also held the same belief about personal control.

More positive attitudes accompanied Fatphobia scores that were on the lower side.

The lowest fatphobia scores were the ones who believed that obesity is more controlled by genetics and the environment. However, only 20% felt this way.

In conclusion, while the survey was not a successful research method in representing primary data in this province, it was still representative of the data found in the secondary sources on this topic.

Upon completing data analysis from the two research methods, it was determined that one of the primary actors, healthcare providers, should be narrowed down to family doctors. This resulted from the finding that most participants experienced the highest amount of weight bias from family doctors. A 2019 study, “Examining Weight Bias among Practicing Canadian Family Physicians,” concluded that negative attitudes towards patients with obesity exist among family physicians in Canada. Many of them reported feelings of frustration with patients with obesity and agreeing that people with obesity increase demand on the public healthcare system (Alberga at al, 2019). So while the survey in this study did not yield representative data, there is sufficient information available on the existence of biases among family doctors. In addition to having access to existing literature, focusing on just family doctors will make synthesizing more manageable since different healthcare providers have different influences, barriers, and characteristics.