Untangling Social Function and Social Cognition: A Review of Concepts and Measurement
Bibliographic record
Abstract
Over the past few decades, there has been increasing interest in the study of social impairment in schizophrenia. However, the concept of social functioning has been poorly defined in the literature. This article highlights the global and multi-factorial nature of social functioning and reviews the theoretical determinants of social dysfunction in schizophrenia. Emphasis is placed on outlining the social cognitive deficits that may occur. The study of social cognition appears particularly promising in elucidating our understanding of the development of social impairment in schizophrenia and has the potential to improve current psychosocial interventions. However, continued advances depend upon the existence of reliable and well-validated measures of social functioning and social cognition. A selection of measures are reviewed in this article in an attempt to highlight the importance of assessing multiple aspects of social functioning in schizophrenia and to assist researchers in the selection of appropriate measures. Future efforts should be directed towards the continued validation of social functioning and social cognitive measures and their adaptation for use in at-risk and early psychosis populations.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".