Social cognition mediates illness-related and cognitive influences on social function in patients with schizophrenia-spectrum disorders
Bibliographic record
Abstract
BACKGROUND: Although cognition has been studied extensively among patients with schizophrenia, social cognition has only recently emerged as an area of interest. The objective of the current study was to use structural equation modelling to test the hypothesis that the relation between cognitive performance and social function is mediated by patients' social cognitive abilities. METHODS: We assessed participants who met criteria for a schizophrenia-spectrum disorder, with equal distribution among first- and multi-episode participants, and nonpsychiatric controls on a range of measures within each of the domains of cognition, social cognition and social function. RESULTS: Using structural equation modelling, we derived a model that explained 79.7% of the variance in social function and demonstrated that the link between cognition and social function was fully mediated by social cognition. LIMITATIONS: A limitation of this study is that the measures contributing to the structural equation modelling analysis were obtained at the same point in time. Thus, the temporal order of causation suggested by Model 2 remains theoretically specified. CONCLUSION: This study provides some first steps in understanding the complex relation between cognition and social function. Such a relation has potential implications for the design of remediation strategies.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".