Some hope for a dimensional assessment? A critical review of psychometric validated (semi‐)structured interview to assess eating disorders
Bibliographic record
Abstract
OBJECTIVE: Given that eating disorders (EDs) are considered one of the deadliest mental illnesses, the development of appropriate assessment instruments is a necessity. Despite the extensive literature on assessing EDs, there has been a lack of focus on semi-structured interviews. The purpose of this article is to provide a comprehensive review of psychometrically validated semi-structured interviews for EDs. METHODS: Included studies (N = 24) were required to present a semi-structured interview for EDs that has been validated through a psychometric process. The APA PsycNet, MEDLINE, APA Psycinfo, Pubmed, and Health & Psychosocial Instruments databases were searched. The literature search included publications through May 2024, with no earliest year restriction. RESULTS: A total of six instruments were identified and reviewed in terms of conceptual design, purpose and content, psychometric characteristics, and strengths and limitations. Three main findings were highlighted: (a) only half of the instruments are up to date; (b) the instruments are based on either a categorical or a mixed categorical-dimensional approach; and (c) the predominance of the categorical approach. CONCLUSIONS: The results are discussed regarding the conceptual approaches of the instrument to provide clinical and research implications. Despite the many strengths of the instrument, additional psychometric research is needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".