The relationship between alexithymia, empathy and moral judgment in patients with multiple sclerosis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Converging research in patients with multiple sclerosis (MS) has shown increased rates of alexithymia and disturbances in social cognition, including empathy and theory of mind. Moral judgment is one of the most complex spheres of human cognition, relying on intricate neural circuits related to many other affective, social, cognitive and behavioral processes. METHODS: Relapsing-remitting MS patients (n = 38) and age-, gender- and education-matched controls (n = 38) completed a measure of alexithymia (Toronto Alexithymia Scale), a measure of empathy (Interpersonal Reactivity Index) and a series of moral dilemmas, for which measures of moral permissibility, emotional reactivity and moral relativity (the perception of how one's moral attitudes compare to the attitudes of the rest of the people) were derived. RESULTS: Relative to controls, patients exhibited decreased levels of other-oriented empathy [empathic concern (P < 0.01) and fantasy (P < 0.01)], increased levels of self-oriented personal distress (P < 0.01), as well as higher rates of alexithymia (P < 0.001). Moral permissibility was significantly reduced in patients with MS (P = 0.038), who also showed higher levels of emotional reactivity (P < 0.01). Additionally, a significantly higher number of patients than controls considered that respondents would deliver similar judgments to the same moral scenarios (P < 0.001). DISCUSSION: Understanding such complex interactions between individual dispositions and moral cognition has the potential to contribute to the development of better assessment and intervention strategies for MS patients, enhancing quality of life by achieving better social participation.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".