Prevalence and Incidence of Eating Disorders in Western Societies
Bibliographic record
Abstract
This chapter reviews the prevalence and incidence of eating disorders (EDs) with a focus on Western societies, specifically the United States, Canada, Europe, and Australia. Prevalence counts cases of illness regardless of onset and, thus, combines both existing and new-onset cases in estimates. Data regarding the prevalence or incidence of EDs are often collected via interviews in which the interviewer makes a diagnosis according to defined criteria. Across the 20th century, anorexia nervosa (AN) incidence appears to have increased. The DSM-IV-TR suggests that women are 10 times more likely to suffer from bulimia nervosa (BN) than men. Binge eating disorder (BED) was a provisional research diagnosis in DSM-IV-TR (2000) and is an ED in DSM-5. Although BED was described decades before BN, less is known about the prevalence and incidence of BED due to its more recent recognition in the DSM.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".