A multicomponent approach toward understanding emotion regulation in schizophrenia
Bibliographic record
Abstract
OBJECTIVES: Emotion deficits are well documented in people with schizophrenia. Far less is known about their ability to implement emotion regulation strategies. We sought to explore whether people with schizophrenia can modify their emotion responses similar to controls. METHODS: People with (n = 25) and without (n = 21) schizophrenia were instructed to amplify positive-emotion expression, reappraise negative emotion experience, and suppress physiological response. Multiple components of emotion response were measured (experience, expression, and physiology). RESULTS: Although people with schizophrenia showed increased positive expressivity following amplification and decreased negative emotion experience following reappraisal, overall, they expressed less positive emotion and experienced more negative emotion compared with controls. Neither group was effective at physiological suppression. CONCLUSIONS: Together these findings suggest that people with schizophrenia can engage in amplification and reappraisal when explicitly instructed to do so, albeit additional practice may be necessary to modify emotion responses to levels similar to controls.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".