Clinical symptomatology and empathy in schizophrenia: Which relationship?
Bibliographic record
Abstract
Introduction The impairment of cognitive and affective empathy among patients with schizophrenia (SCZ) may represent a significant feature of the illness. However, the relationship between those impairment and dimensions of psychosis remains unclear. Objectives To explore whether cognitive and affective empathy are associated with severety of different psychotic symptoms. Methods Cognitive and affective empathy were evaluated in 58 patients with stable schizophrenia with the Questionnaire of Cognitive and Affective Empathy (QCAE) comprising five subscales intended to assess cognitive and affective components of empathy. Symptomatology evaluation comprised the Positive and Negative Syndrome Scale (PANSS), the Calgary Depression Scale for Schizophrenia (CDSS) and the Clinical Global Impressions Scale Improvement and severity (CGI). Results Patients with better cognitive empathy had less total CDSS scores (P = 0.036, r = −0.449) and lower CGI-severity scale scores (P = 0.01, r = −0.536). Patients with better affective empathy had lower scores (which means a better improvement) at the CGI-improvement scale (P = 0.03, r = −0.461). Conclusions Our results suggest that empathy with its different component is not totally independent of the clinical state of the patient. Further studies are required to confirm whether empathy deficits are state or trait aspects of SCZ. Disclosure of interest The authors have not supplied their declaration of competing interest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".