Improving Adult Inpatient Eating Disorder Treatment: Perspectives of a Sample of Canadians with Lived Experience
Bibliographic record
Abstract
Abstract Background Eating disorders (EDs) are common, affecting over one million Canadians. Canadian adults (i.e., age 18+) requiring medical stabilization for their eating disorder (ED) may be referred to adult inpatient (IP) ED treatment for care. Recent Canadian publications have brought attention to the need for improved approaches to Canadian ED treatment; urging researchers to seek perspectives of people with lived experience to determine how to best do so. This study explored the perspectives of Canadians with lived experience to identify recommendations for improvement of adult IP ED treatment programs and processes. Methods Employing a transformative philosophical view and feminist standpoint theory, this study utilizes a qualitative hermeneutic phenomenological approach to fulfill the objectives. Eleven participants with lived adult IP ED treatment experiences from across Canada were interviewed individually, to discuss their experiences and recommendations regarding referral, transitions into and out of care, and treatment itselfusing an online video conference platform. Data were analyzed using interpretative phenomenological analysis. A comprehensive list of recommendations was drafted and brought back to participants for feedback. The feedback was implemented to create the final list of recommendations. Results Several limitations of referral, transitions, and treatment, facilitated and exacerbated by stigma at individual and societal levels, were identified by participants. These included guilt and shame upon referral, lack of respect and trust from healthcare providers during transitions, and lack of consideration of social determinants of health during treatment. Participant-informed recommendations, which can be categorized as interim support, individualized care, dignified treatment, resources, and stigma, were identified to ameliorate the experiences of Canadians with EDs while also combatting stigma. Conclusions Adult IP ED treatment in Canada is in urgent need of significant change to meet the needs of those requiring care and to address harmful stigma. Implementing participant-informed recommendations may aid in achieving this goal. The meaningful inclusion of those with lived experience, particularly marginalized populations, will be paramount to the development of an approach to adult IP ED treatment that properly serves Canadians who need it.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".