An Exploration of Eating Disorders in Canada: Towards Equitable Access to Care
Bibliographic record
Abstract
At any given time, approximately 1.5 million Canadians meet the diagnostic criteria for anorexia or bulimia, not including individuals experiencing other eating disorder subtypes or those who are in recovery. Eating disorders are serious mental health conditions linked to severe distress, deterioration of physical health, mental health comorbidities, and loss of life. Individuals of different cultural backgrounds, genders, ages, and socio-economic status are all at risk of developing eating disorders, yet not all are able to access eating disorder diagnoses and treatment equally. The goal of this dissertation is to contribute towards expanding eating disorder care options for care-seekers, with particular attention to those left out of formal treatment options, such as Latinx individuals. Towards this goal, my work research focuses on eating disorders in Latinx individuals in Canada and care options available to people who may not be able to access clinical care. Research in these areas in a Canadian context is sparse. Thus, this project uses a variety of research methods and methodologies to address gaps in understanding eating disorder care options outside of clinical treatment and the case of eating disorders among Latinx individuals in Canada. I first present quantitative work on correlates of eating disorders to provide data on the prevalence of eating disorders in racialized population groups in Canada. I then present findings on barriers to eating disorder care identified by non-profit eating disorder organizations in Canada. I also show that these eating disorder organizations play an important role in expanding access to care by helping individuals access clinical treatment and by providing non-clinical care alternatives. Finally, I present work produced through a community-based project, in partnership with a Latinx organization in Toronto. Together, this body of work demonstrates a continued need for research on assessing the extent to which certain individuals are unable to access timely, appropriate, and affordable eating disorder treatment. Additionally, my findings highlight an urgent need to provide reliable and robust funding for eating disorder–related work, and in particular expand funding for eating disorder organizations in Canada, given their critical role in improving access to eating disorder care.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.021 | 0.004 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".