A Structural Equation Modelling Exploration of the Role of Schizotypal Traits, Cognitive Schemas and Dysfunctional Attitudes in Social Isolation
Bibliographic record
Abstract
INTRODUCTION: Evidence highlights the importance of social isolation as a critical yet underserved treatment target for individuals managing psychosis. Schizotypal traits represent a useful model of psychosis, facilitating the assessment of contributors to social isolation without the confounds associated with schizophrenia. This study utilised structural equation modelling to examine the unique predictive capacity of schizotypal traits for subjective and objective indices of social isolation. In addition, the potentially mediating role of negative core schemas and dysfunctional attitudes was assessed. METHODS: Structural equation modelling was used to measure and compare the relationships between the constructs of interest simultaneously. RESULTS: Satisfactory fit indices were attained with separate models predicting loneliness and social engagement. Results support the partial mediation of the relationships between positive and negative traits, internalising symptoms and loneliness. While all three direct pathways were significant, all three were partially mediated. Of note, these mediated effects were not observed in the model predicting social engagement, with the only significant pathways being those directly from positive and negative schizotypal traits. CONCLUSIONS: Schizotypal traits directly predict loneliness and social engagement above that accounted for by internalising symptoms. Cognitive factors partially mediate the relationships between schizotypy and loneliness but not the size of an individual's social network. Cognitive interventions may be well suited for reducing loneliness; however, other approaches may be required to increase social networks for individuals with high levels of schizotypy.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".