Impact of psychosocial stress on facial emotion recognition in schizophrenia and controls: an experimental study in a forensic sample
Bibliographic record
Abstract
Introduction: Psychotic disorders have been associated with dysregulated stress reactions and adaptation. Little is known about the neuroendocrine responses to psychosocial stress in justice-involved individuals with schizophrenia. Methods: Using an experimental research design, the present study aims to examine differences in the subjective and neuroendocrine responses to psychosocial stress and its impact on facial emotion recognition (FER) and performance on an arithmetic task in chronically ill justice-involved individuals with schizophrenia (PAT) and a healthy control group. PAT undergoing treatment in forensic psychiatric inpatient wards (n = 17) and a healthy control group (n = 17) were assessed regarding sociodemographic and clinical characteristics. Additionally, salivary cortisol levels, measured before and after performing a psychosocial stress task [Montreal Imaging Stress Task (MIST)], and performance on an arithmetic problem-solving task and two FER tasks were recorded. Two participants dropped out, one from each group. Therefore, the final sample consisted of 32 individuals. Results: Significant group differences in FER were recorded. There was a significant rise in subjective perception of momentary strain relating to the induction of psychosocial stress in both groups. Notably, the pre-stress level of subjective strain was higher in the PAT group than controls. Acute psychosocial stress induced an increase in FER performance in a sub-task related to naming emotions in individuals with schizophrenia spectrum disorder. Discussion: The results underline the importance of psychosocial and therapeutic interventions aimed at strengthening stress resilience in individuals with schizophrenia spectrum disorders.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".