Perceptions of genetic risk, testing, and counseling among individuals with eating disorders
Bibliographic record
Abstract
OBJECTIVE: Eating disorders develop as a result of genetic and environmental factors. Given that they are multifactorial conditions with a genetic component, they fall within the scope of practice for genetic counseling, but people with these conditions are rarely referred. The purpose of this study was to explore the perceptions of causes of eating disorders, recurrence risk, and interest in genetic counseling and testing among individuals with eating disorders. METHOD: An online survey comprising both multiple choice and free form text questions, vignettes about genetic counseling, and the ED100K (validated eating disorder diagnostic questionnaire) was shared via support organizations and prominent bloggers in the eating disorders community to recruit individuals with a personal history of an eating disorder from November 2018 to February 2019. RESULTS: In total, 107 participants completed the survey. They perceived that both experiences and genetics were important factors in the development of their eating disorder. All responding participants overestimated the risk for recurrence of eating disorders in children, often by a large margin, and a notable minority reported that their experience with an eating disorder had a negative influence on their childbearing decisions. After imagined experience of genetic counseling, participants reported significantly decreased feelings of stigma, shame, and guilt. Most participants expressed interest in genetic counseling; fewer were interested in genetic testing. DISCUSSION: Genetic counseling may benefit individuals with eating disorders by providing accurate recurrence risk information and reducing feelings of guilt, stigma, and shame, which may in turn encourage earlier support seeking and recovery.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".