Screening High School Students for Eating Disorders: Validity of Brief Behavioral and Attitudinal Measures
Bibliographic record
Abstract
BACKGROUND: Early identification can greatly impact the trajectory of eating disorders, and school-based screening is 1 avenue for identifying those at risk. To be feasible in a school setting, a screening program must use a brief, valid screening tool. The aim of this study was to assess how well brief attitudinal and behavioral survey items identify adolescents at risk in a large sample of high school students from across the United States. METHODS: Data were drawn from the National Eating Disorder Screening Program, the first-ever national eating disorders screening initiative for US high schools. A 2-stage, clustered sampling method was used to randomly select a subset of student screening forms (n = 5740), which included the Eating Attitudes Test (EAT-26), behavioral questions assessing the frequency of vomiting and binge eating in the past 3 months, and an attitudinal item that assessed preoccupation with thinness. RESULTS: Nearly 12% of females and 3% of males reported vomiting to control their weight and 17% of females and 10% of males reported binge eating 1 or more times per month. Approximately 24% of females and 8% of males report being preoccupied with being thinner. We found that the attitudinal measure yielded high sensitivity and specificity. Combined screening measures that used both the attitudinal and behavioral items yielded slightly higher sensitivity values than those found with the attitudinal measure alone. CONCLUSION: High school administrators should include items that assess both preoccupation with thinness as well as behavioral items that deal with eating disorders on student health surveys.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".