Emotion dysregulation in older people: validity and reliability of an 8-item version of the Difficulties in Emotion Regulation Scale
Bibliographic record
Abstract
Objectives The abbreviated 16-item version of the Difficulties with Emotion Regulation Scale (DERS-16) is widely used to assess individuals’ perceived challenges in regulating their emotions, a central aspect of psychological symptoms commonly experienced in old age. However, its psychometric properties have yet to be tested in this population. Furthermore, a shorter version of the DERS-16 could further minimize the assessment burden on older individuals. Thus, we aimed to test the DERS-16’s psychometric performance and determine if any items were redundant to develop a psychometrically sound shorter version.Methods We enrolled 302 Portuguese older adults (Mage = 75.22; SD = 8.99 years) in a cross-sectional study.Results Exploratory factor analyses indicated a one-factor structure and a four-factor solution with eight items (69.3%–81.9% of the variance observed). The four-factor–8-item solution presented an interpretable structure and demonstrated good reliability values (> 0.70) and construct validity with the Twenty-Item Toronto Alexithymia Scale, Eight-Item Geriatric Depression Scale, and Geriatric Anxiety Inventory (r = 0.66, 0.40, 0.52; p < 0.001).Conclusion The robust psychometric properties of DERS-8 make it a valuable tool for clinical and longitudinal studies, facilitating targeted interventions in older adults and allowing for precise emotion dysregulation screening.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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".