Psychometric properties of the difficulties in emotion regulation Scale in a perinatal sample
Bibliographic record
Abstract
BACKGROUND: One in five pregnant and postpartum individuals experience an anxiety, depressive, and/or trauma-related disorder. Emotion dysregulation (ED) underlies the development and maintenance of various mental health disorders. The Difficulties in Emotion Regulation Scale (DERS) is the most comprehensive and commonly used measure of emotion dysregulation, yet limited evidence supports its use in the perinatal population. The present study aims to evaluate the validity of the DERS and its six subscales in a perinatal sample and to assess its predictive utility in identifying perinatal individuals with a disorder characterised by emotion dysregulation. METHODS: = 237) completed a diagnostic clinical interview and self-report measures of anxiety, depression, and perceived social support. RESULTS: The DERS subscales demonstrated good internal consistency and construct validity, as it strongly correlated with measures of anxiety and depression and failed to correlate with a measure of perceived social support. Results from an exploratory factor analysis supported a 6-factor solution, suggesting structural validity. An ROC analysis revealed good to excellent discriminative ability for the DERS full scale and four of the subscales. Finally, an optimal clinical cut-off score of 87 or greater was established with a sensitivity of 81% for detecting a current anxiety, depressive, and/or trauma-related disorder. CONCLUSIONS: This study provides evidence for the validity and clinical utility of the DERS in a treatment-seeking and community sample of pregnant and postpartum individuals.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".