What Is the Effect of Attributing Disordered Eating Behaviours to Food Addiction Versus Binge Eating Disorder? An Experimental Study Comparing the Impact on Weight-Based and Mental Illness Stigma
Bibliographic record
Abstract
Background/Objectives: Food addiction (FA) and binge eating disorder share many overlapping features. Many individuals with binge eating disorder experience stigma; however, less is known about the stigma associated with food addiction. The current study examined the weight-based stigma and mental illness stigma associated with attributing disordered eating behaviours to an FA diagnosis or binge eating disorder diagnosis. Methods: Undergraduate students (N = 177) were randomly assigned to read one of three vignettes (FA, binge eating disorder, or control), all of which described a character experiencing the overlapping features of FA and binge eating disorder; the vignettes differed only regarding the diagnosis to which the eating behaviours were attributed. Participants then completed questionnaires assessing their attitudes towards mental illness and obesity followed by questionnaires assessing their own eating behaviours. Results: There were no significant between-group differences in mental illness stigma or weight-based stigma. Significant differences in stigma were found based on the perceived gender of the vignette character and participants’ own FA and binge eating disorder symptoms. Conclusions: Stigma may not differ based on the diagnosis ascribed to addictive-like eating behaviours. Women may be more stigmatized for addictive-like eating behaviours, and individuals who experience addictive-like eating may be more stigmatizing towards others with these behaviours.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".