Exploring the Relationship Between Childhood Maltreatment, Alexithymia, and Facial Emotional Recognition in Schizophrenia
Bibliographic record
Abstract
BACKGROUND: Childhood maltreatment is a significant factor affecting social cognition in schizophrenia (SCZ) patients. However, the relationship between childhood maltreatment, alexithymia, and facial emotional recognition in SCZ remains unclear. METHODS: SCZ patients in stable phase (n = 90) and healthy controls (n = 47) were included according to the DSM-5 criteria. Clinical symptoms were assessed using the Positive and Negative Syndrome Scale (PANSS), childhood maltreatment was evaluated using the Childhood Trauma Questionnaire (CTQ), and alexithymia was assessed using the Toronto Alexithymia Scale-20 (TAS-20) to evaluate the ability to identify, describe, and express emotions. Social cognition was assessed using the Facial Emotion Recognition Test. RESULTS: Our findings indicate significant differences in CTQ, TAS-20, and facial emotional recognition between the two groups, with the SCZ group showing more severe impairments. Pearson correlation analysis showed that correct facial emotion recognition was negatively correlated with childhood maltreatment and alexithymia (p < 0.05). Stepwise regression analysis further revealed that the total PANSS score, positive symptom, CTQ total score, and difficulty describing feelings negatively affected the accuracy of facial emotional recognition (p < 0.05). Patients who find it difficult to describe feelings may also have greater difficulty in recognizing facial expressions of anger. CONCLUSION: Good psychosocial functioning can mitigate the negative impact of childhood maltreatment on the severity of illness in SCZ patients. Therefore, psychotherapy that promotes personal expression is useful in the treatment of SCZ.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".