Practical Considerations for Using the Eating Disorder Examination Interview with Adolescents
Bibliographic record
Abstract
Megan N Parker,1 Jason M Lavender,2– 4 Natasha A Schvey,1 Marian Tanofsky-Kraff1– 3 1Department of Medical and Clinical Psychology, Uniformed Services University of the Health Sciences, Bethesda, MD, USA; 2Department of Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, USA; 3Military Cardiovascular Outcomes Research (MiCOR) Program, Bethesda, MD, USA; 4The Metis Foundation, San Antonio, TX, USACorrespondence: Jason M Lavender, Military Cardiovascular Outcomes Research Program, 4301 Jones Bridge Road, Building 17, Suite 2A, Bethesda, MD, 20814, USA, Email jason.lavender.ctr@usuhs.eduAbstract: Approximately 35 years after its initial publication, the Eating Disorder Examination (EDE) remains one of the most widely used semi-structured interviews for assessing eating disorder diagnoses and symptomatology. Although the interview provides certain advantages over other common measurement approaches (ie, questionnaires), there are particular considerations regarding the EDE that warrant attention, including in its use with adolescents. The aims of this paper are therefore to: 1) provide a brief overview of the interview itself, as well as a description of its origin and underlying conceptual framework; 2) describe relevant factors for administering the interview with adolescents; 3) review potential limitations regarding use of the EDE with adolescents; 4) address considerations for using the EDE with pertinent subpopulations of adolescents who may experience distinct eating disorder symptoms and/or risk factors; and 5) discuss the integration of self-report questionnaires with the EDE. Advantages of using the EDE include the ability for interviewers to clarify complex concepts and mitigate inattentive responding, enhanced orientation to the interview timeframe to improve recall, increased diagnostic accuracy compared to questionnaires, and accounting for potentially salient external factors (eg, food/eating rules imposed by a parent/guardian). Limitations include more extensive training requirements, greater assessment burden, variable psychometric performance across subgroups, lack of items evaluating muscularity-oriented symptoms and avoidant/restrictive food intake disorder diagnostic criteria, and lack of explicit consideration for salient risk factors other than weight and shape concerns (eg, food insecurity).Keywords: eating disorders, disordered eating, youth, semi-structured, measurement, assessment
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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 | 0.419 | 0.573 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".