Gender Differences in Social Cognition: A Cross-Sectional Pilot Study of Recently Diagnosed Patients with Schizophrenia and Healthy Subjects
Bibliographic record
Abstract
Objectives: This study had 2 objectives: First, to explore the gender-related differences in emotional processing (EP) and theory of mind—both cognitive (CToM) and affective (AToM)—in patients with schizophrenia and in a control group of healthy subjects; and, second, to examine, from a gender perspective, the possible association between EP and CToM in the AToM performance. Methods: Forty patients with schizophrenia/schizoaffective disorder were recruited and matched by gender, age and years of education with 40 healthy subjects. EP was measured by the pictures of facial affect (POFA) test. CToM was measured using first- and second-order false-belief (FB) stories. AToM was measured by the reading the mind in the eyes test (RMET). Group and gender differences in CToM were analysed using the X 2 test, whereas EP and AToM were analysed using the non-parametric Mann–Whitney U Test and a general linear model. Results were adjusted by intelligence quotient and negative symptomatology. Results: Patients with schizophrenia underperformed against healthy subjects in the POFA test, second-order FB, and RMET, but not in first-order FB. No significant gender differences were found. However, there was a trend showing that females outperformed males in the POFA ( P = 0.056). Group ( P < 0.001), POFA ( P < 0.001) and second-order FB ( P = 0.022) were the best factors predicting RMET performance (adjusted R 2 = 0.584). Conclusions: Our results suggest that the illness is the main factor related to the deficit in social cognition, except for the basic aspects of the CToM that were unimpaired in most patients. Nevertheless, the influence of female gender in EP should not be neglected in any group. Finally, the hierarchal interaction between these domains is discussed.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".