Explanatory Factors for Disease-Specific Health-Related Quality of Life in Women with Anorexia Nervosa
Bibliographic record
Abstract
A better understanding of explanatory factors for disease-specific health-related quality of life (HRQoL) in anorexia nervosa (AN) could help direct treatment providers to aspects of the most relevance for patient wellbeing and recovery. We aimed to investigate whether factors associated with HRQoL are the same for women with AN and normal-weight controls. The participants in this study were women with AN recruited from specialized eating disorder centers in Denmark and healthy, normal-weight controls invited via online social media. Participants completed online questionnaires on medical history, disease-specific HRQoL (Eating Disorders Quality of Life Scale, EDQLS) and generic HRQoL (SF-36), eating disorder symptomatology, depression, psychological wellbeing, and work and social adjustment. Questionnaires were fully completed by 211 women with AN (median age 21.7 years) and 199 controls (median age 23.9 years). Women with AN had poorer scores on all measures, i.e., worse HRQoL, psychological health, and work/social functioning. Eating disorder symptomatology affected EDQLS score in both groups, but poorer HRQoL in women with AN was also significantly associated with worse scores on bulimia, maturity fears, depression, vitality, and with older age. The factors investigated together explained 79% of the variance in EDQLS score. Management of disordered self-assessment and thought processes may be of particular importance to women with AN. Greater emphasis on these aspects alongside weight gain could enhance patient-clinician alliance and contribute to better treatment outcomes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".