Experiences of having an eating disorder and perceptions of its etiology: A qualitative study to inform genetic counseling practice.
Bibliographic record
Abstract
There has been limited research regarding how people with eating disorders (EDs) perceive the causes of these conditions. We conducted an interpretive description study to explore the perceived causes of EDs among individuals with a history of EDs, and to examine how healthcare professionals (HCPs) can provide better care for people with these conditions. We interviewed 15 diverse individuals, with emphasis on gender and racial diversity, with a history of EDs about their perceived etiologies of their EDs, what they wished the public and HCPs knew about EDs, and their thoughts on a 3-min video explaining the multifactorial etiology of EDs. After "line-by-line" coding, themes were used to inductively develop a model to describe individuals' experiences of EDs. Participants' narrative about their experiences of their EDs focused on the condition arising from multiple identity- and environment-related causes, which contributed to a negative cycle of emotions that led to feelings of isolation. Perceptions of the cause of EDs and access to treatment were influenced by systemic issues (e.g., racism, sexism), lack of knowledge about, and stigma associated with EDs. Participants wanted personalized care that acknowledges the factors they perceive to contribute to their ED. Our findings support previous work-showing that the causes of EDs are perceived to be complex, and that shame, stigma, guilt, and self-denial are barriers to treatment. It is important for HCPs to provide holistic, empathic care for people with EDs and acknowledge the systemic issues that affect EDs.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| 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".