“It’s beautiful and it’s messy and it’s tragic”: exploring the role of compassion in the eating disorder recovery processes of 2S/LGBTQ + Canadians
Bibliographic record
Abstract
This research explores experiences of compassion among 2S/LGBTQ + Canadians living with eating disorders in the context of eating disorder treatment and community support. There is a growing body of scholarship showing disparities in eating disorder care for those within 2S/LGBTQ + communities. Among the reported concerns is a potential lack of compassion in eating disorder treatment and recovery settings, something which may serve to exacerbate feelings of isolation and perpetuate misunderstandings of 2S/LGBTQ + people's experiences. In an effort to understand these dynamics more deeply, we conducted semi-structured interviews with 2S/LGBTQ + Canadians who have experienced eating disorder care. The data collected were then subjected to Foucauldian discourse analysis, which produced three interconnected discursive considerations: feeling lack of structural compassion, 2S/LGBTQ + communities as places of respite, and 2S/LGBTQ + caregiving. One of the common threads among these discursive considerations was cis-heteronormativity ingrained in eating disorder treatment settings and health care systems more broadly. Our findings underscore the critical need for more enhanced compassion for 2S/LGBTQ + patients in eating disorder care settings. We conclude that compassion, when implemented on the levels of individual clinicians, policy and procedure, and institutions, may represent an avenue toward disrupting ingrained cis-heteronormativity and the associated discursive power structures contained in health care systems.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.038 | 0.025 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".