Moderators of emotion regulation abnormalities at the identification, selection, and implementation stages in schizophrenia
Bibliographic record
Abstract
Individuals with schizophrenia (SZ) experience difficulties across stages of emotion regulation. However, moderators of abnormalities at each stage have not been systematically examined, limiting the development of mechanism-based treatment approaches. In the current study, outpatients with SZ (n = 52) and healthy controls (CN) (n = 55) completed six days of ecological momentary assessment (EMA) assessing emotional experience, emotion regulation, and moderators including: arousal, emotional awareness, ability to describe emotions, interoception, acceptance, emotion regulation knowledge, and cognitive ability. For identification, emotional awareness and the ability to describe emotions moderated the initiation of emotion regulation in the CN group but not SZ. For selection, interoceptive awareness moderated the frequency of selecting individual strategies in CN only. For implementation, arousal, emotional awareness, and ability to describe emotions improved the effectiveness of down-regulating negative affect in CN but not SZ. Findings suggest that arousal, interoception, emotional awareness, and the ability to describe emotions facilitate emotion regulation processes at the three stages among CN but not SZ. This is consistent with theories positing that SZ engage in contextually insensitive emotion regulation. Findings are discussed in relation to selecting intervention targets tailored to mechanisms underlying abnormalities at each stage of emotion regulation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".