A study of empathic deficits and its correlative factors in schizophrenic patients
Bibliographic record
Abstract
Objective To explore the peculiarities and affected factors of empathic deficits of the patients with schizophrenia.Methods We assessed multiple dimensions of empathy in 208 schizophrenic patients and 213 healthy controls with a self-rating instrument,the interpersonal reactivity index-C(IRI-C).All the patients were rated on Eysenck personaliy questionnaire(EPQ),The self-esteem scale(SES),general self-efficacy scale(GSES),simplified coping style questionnaire(SCSQ),social support rating scale(SSRS),Toronto alexithymia scale(TAS-20)and the positive and negative symptom scale(PANSS).Results Compared with healthy controls,schizophrenic patients showed significantly lower total scores,and subscores in perspective taking,fantasy and empathic concern of IRI-C(P0.01).The neuroticism(r=-0.22)and psychoticism scores(r=-0.18)of EPQ,the factor III scores(r=-0.30)of TAS,the positive coping style scores(r=0.21)of SCSQ,the subjective supporting scores(r=0.16)of SSRS and the scores(r=0.21)of GSES had correlations with the total scores of IRI-C(P 0.05).The regression analyss with the total scores of IRI-C as dependent variable showed that the positive coping style subscore of SCSQ,The neuroticism and psychoticism subscores of EPQ,and the factor III subscore of TAS had come into the regression model,and the standard coefficients were 0.28、0.30、-0.18、-0.18 respectively.Conclusions Schizophrenic patients had a extensive impairment of empathic abilities,which was affected by the personality and coping styles.
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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 | 0.000 | 0.001 |
| 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".