Fattening up Health Care: Exploring the Ways Fat Women Navigate Health Care Services in Canada
Bibliographic record
Abstract
This dissertation explores and documents how fat women in Canada experience fatphobia in health care settings, focusing largely on primary care. This study, which is based on interviews and focus groups with fat women, asks broadly: How does fatness act as a barrier to accessing health care services for fat women in Canada? To answer this question, I explore the following four sub questions: (1) How has fatness come to be socially constructed as a moral panic of an obesity epidemic, resulting in the medicalization of fatness?; (2) How does the framing of fatness as an obesity epidemic impact the relationship fat women have with their bodies and themselves?; (3) With a focus on primary care physicians, how does the advice of medical professionals impact fat womens perceptions of their bodies and their health?; and (4) How does the categorization of obesity as a disease by Obesity Canada, in the 2020 Canadian Adult Obesity Clinical Practice Guidelines, further entrench fatphobia in health care practice?\nWorking at the intersections of fat studies, sociology of health, and feminist standpoint epistemology, I argue that fatness is a barrier to accessing health care services in Canada. Through the experiences of my participants, I find that the framing of fatness as an obesity epidemic has resulted in fat women having antagonistic relationships with their bodies, understanding their bodies as moral failures. These feelings carry over to health care spaces where practitioners often hold anti-fat bias, resulting in weight-based discrimination and experiences of fatphobia in health care. Finally, despite an abundance of research calling for health care professionals to re-consider and re-frame their approaches to fatness in health care settings, health care professionals are ignoring the research on anti-fat bias and instead are doubling down on obesity as disease.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.049 | 0.022 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".