Comparison of Single- and Double-Stage Designs in the Prevalence Estimation of Eating Disorders in Community Samples
Bibliographic record
Abstract
The aim of this research was to compare two different case-identification designs: (a) a one-stage anonymous design using the Eating Disorders Examination-Questionnaire (EDE-Q; Fairburn & Beglin, 1994) as diagnostic instrument and (b) a two-stage-non-anonymous design using the Eating Attitudes Test (EAT; Garner & Garfinkel, 1979) and the EDE-Q as screening instruments and the clinical interview Eating Disorders Examination (EDE; Fairbumrn & Cooper, 1993) as diagnostic instrument, in the estimation of eating disorders prevalence in community samples. Both epidemiological designs were compared in: eating disorders prevalence, population at risk, and weekly frequency of associated symptomatology (binge eating episodes, self-vomiting) within a sample of 559 scholars (14 to 18 year-old males and females) studying in the region of Madrid. Eating disorders prevalence estimation using single-stage design was 6.2%, and 3% using the two-stage design; however, these differences were not significant (p = .067). No significant differences between the two procedures were found either in population at risk or in weekly frequency of reported self-vomiting. Reported binge eating episodes were higher in the one-stage design. The use of a two-stage procedure with clinical interview (vs. questionnaire) leads to a better understanding of the items (specially the most ambiguous ones) and thus, to a more accurate prevalence estimation.
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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 | 0.090 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".