Exploring the links between alexithymia, empathy and schizotypy in college students using network analysis
Bibliographic record
Abstract
Introduction: Impaired empathy is one of the major dysfunctions commonly found in patients with schizophrenia, with alexithymia being one possible underlying factor. Schizotypy represents a set of psychotic-like manifestations, investigation of which may contribute to our understanding of psychosis while minimising the confounding effects of illness chronicity and medication exposure. Few studies have specifically examined the associations among alexithymia, empathy and schizotypy.Methods: We investigated the relationships among alexithymia, empathy and schizotypy in college students using network analysis. The Interpersonal Reactivity Index (IRI), Toronto Alexithymia Scale (TAS), and Chapman Psychosis-Proneness scales were captured, and network based on the subscales were estimated in 552 participants. Strength, closeness and betweenness of nodes were calculated to measure the centrality.Results: Network analyses revealed a pattern connecting alexithymia with empathy and schizotypy. Negative connections between empathy and physical/social anhedonia and positive edges linking alexithymia with empathy and social anhedonia were observed.Conclusions: Network constructed in the study demonstrated alexithymia’s role in empathic deficits. Our findings highlighted the connections between components of empathy, alexithymia and schizotypy.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".