Weight misperception and its associations with eating disorder symptoms over the course of residential eating disorder treatment
Bibliographic record
Abstract
OBJECTIVE: Although individuals with eating disorders (EDs) often experience significant body dissatisfaction and perceptual body image distortions, the presence and impact of weight misperception in clinical samples have been minimally examined. The aims of this study were to quantify weight misperception in individuals with EDs, examine whether weight misperception predicts ED severity at treatment discharge, and explore changes in weight misperception across treatment. METHOD: Participants were 98 women seeking residential treatment for their ED who reported weekly on their perceived weight. Objectively measured weight was subtracted from perceived weight to determine weekly "weight misperception." Participants completed the Eating Disorder Examination Questionnaire (EDE-Q) at intake and discharge to assess ED severity. Weight misperception at intake and change in weight misperception over treatment were examined as predictors of ED pathology at discharge. RESULTS: Approximately 74.5% of the sample overestimated their weight, with an average weight misperception of 2.7 (SD = 5.6) pounds (1.2 kg; SD = 2.5). Weight misperception spanned from -6.2 to 43.6 pounds (-2.8 to 19.8 kg) and did not differ based on ED diagnosis. On average, weight misperception increased throughout treatment. Greater weight misperception at intake as well as greater increases in weight misperception over treatment significantly predicted EDE-Q scores at discharge. DISCUSSION: Findings highlight that weight misperception is not limited to underweight patients. Misperceiving one's weight may predict symptom severity across a range of EDs, and future research is needed to examine whether targeting weight misperception during residential treatment could improve treatment outcomes for individuals with 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 | 0.001 | 0.006 |
| 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 | 0.000 | 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".