Emotion Regulation Diversity in Current and Remitted Depression
Bibliographic record
Abstract
Depression is associated with reduced flexibility in emotion regulation (ER). Diversity in the use of ER strategies is crucial for ER flexibility. In this study, we examined associations between depression and ER diversity and proposed a novel measure: the ER diversity index. Currently depressed ( n = 58), remitted depressed ( n = 65), and healthy control participants ( n = 55) rated their use of nine ER strategies. Four ER measures were computed (diversity index, sum score, flexibility score, intraindividual standard deviation), and their association with diagnostic group was compared. The ER diversity index was associated with depression status more strongly than all other ER measures. Currently and remitted depressed individuals exhibited greater diversity in ER strategies overall and maladaptive ER strategies but less diversity in adaptive ER strategies compared with healthy individuals. Thus, the ER diversity index may be a valid measure of ER diversity, and ER diversity may have an important role in depression.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| 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".