Psychometric properties of the Farsi version of the Eating Pathology Symptoms Inventory (F-EPSI) among Iranian University men and women
Bibliographic record
Abstract
BACKGROUND: Limited research has validated eating pathology assessments in Iranian men and women. The purpose of the current study was to translate and validate a Farsi version of the Eating Pathology Symptoms Inventory (F-EPSI) in Iranian university students. METHODS: Men (n = 279) and women (n = 486) completed questionnaires including the F-EPSI. RESULTS: Confirmatory factor analysis (CFA) indicated that the F-EPSI had an acceptable fit to the data and supported the eight-factor model. The scale was partially invariant across genders. Men reported higher scores on Excessive Exercise and Muscle Building subscales, and women reported higher scores on Body Dissatisfaction and Restricting subscales. The F-EPSI subscales had good 5- to 6-month test-retest reliability. The F-EPSI demonstrated convergent validity with clinical impairment, eating pathology, and body mass index (BMI). Finally, individuals scoring above the Clinical Impairment Assessment (CIA) cutoffs reported higher scores on the F-EPSI subscales, further supporting convergent validity of the scale. CONCLUSION: Findings suggest that the F-EPSI will enable researchers to examine eating pathology symptoms among men and women in Iran.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".