Negative Symptoms and Avoidance of Social Interaction: A Study of Non-Verbal Behaviour
Bibliographic record
Abstract
BACKGROUND: Non-verbal behaviour is fundamental to social interaction. Patients with schizophrenia display an expressivity deficit of non-verbal behaviour, exhibiting behaviour that differs from both healthy subjects and patients with different psychiatric diagnoses. The present study aimed to explore the association between non-verbal behaviour and symptom domains, overcoming methodological shortcomings of previous studies. SAMPLING AND METHODS: Standardised interviews with 63 outpatients diagnosed with schizophrenia were videotaped. Symptoms were assessed using the Clinical Assessment Interview for Negative Symptoms (CAINS), the Positive and Negative Syndrome Scale (PANSS) and the Calgary Depression Scale. Independent raters later analysed the videos for non-verbal behaviour, using a modified version of the Ethological Coding System for Interviews (ECSI). RESULTS: Patients with a higher level of negative symptoms displayed significantly fewer prosocial (e.g., nodding and smiling), gesture, and displacement behaviours (e.g., fumbling), but significantly more flight behaviours (e.g., looking away, freezing). No gender differences were found, and these associations held true when adjusted for antipsychotic medication dosage. CONCLUSIONS: Negative symptoms are associated with both a lower level of actively engaging non-verbal behaviour and an increased active avoidance of social contact. Future research should aim to identify the mechanisms behind flight behaviour, with implications for the development of treatments to improve social functioning.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".