Psychometric Properties of the Emotional Processing Scale in Individuals with Psychiatric Symptoms and the Development of a Brief 15-Item Version
Bibliographic record
Abstract
Abstract Objective The 25-item Emotional Processing Scale (EPS) is a measure of emotional processing. There is a scarcity of research about its factor structure, test-retest reliability, and validity in individuals with psychiatric symptoms. Method We administered the EPS-25 to a sample (N=512) of people with elevated psychiatric symptoms, using confirmatory factor analysis to evaluate three a priori models from previous research. We then evaluated discriminant and convergent validity against measures of alexithymia (Toronto Alexithymia Scale-20; TAS-20), depressive symptoms (Patient Health Questionaire-9; PHQ-9) and anxiety symptoms (Generalized Anxiety Disorder-7; GAD-7). Results None of the a priori models achieved acceptable fit, and subsequent exploratory factor analysis did not lead to a convincing factor solution for the 25 items. A 5-factor model did, however, achieve acceptable fit if we retained only 15 items (EPS-15) and this solution could be replicated in a holdout sample. Convergent and discriminant validity for EPS-15 was r=-.19-.46 vs. TAS-20, r=.07-.25 vs. PHQ-9, r=.29-.57 vs. GAD-7. Test-retest reliability was ICC=.73. Conclusions This study strengthens the case for the reliability and validity of the five factor Emotional Processing Scale but suggest that only 15 items should be retained. Future studies should further examine the reliability and validity of the EPS-15.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".