Alexithymia, Social Anhedonia, and Empathy among Client with Schizophrenia: Mediation Model
Bibliographic record
Abstract
Background and Aim: According to earlier studies, schizophrenia is often characterized by socialwithdrawal and a lack of emotional connection. Alexithymia, and social anhedonia significantly impactsthe quality of life for individuals with schizophrenia. If empathy is indeed a mediator between alexithymia,and social anhedonia, then it becomes a potential target for therapeutic interventions. Training programsor treatments designed to enhance empathy could have a significant impact on the social functioning ofindividuals with schizophrenia. However, limited researches have explored this mediation role of empathyin relationship between alexithymia and social anhedonia among individuals with schizophrenia. So, thisstudy aimed to investigate the mediating role of empathy in relationship between alexithymia and socialanhedonia among individuals with schizophrenia. Subjects& Methods: A descriptive correlationalanalytical design was used. on 170 randomly chosen participants who met the selection criteria. Four toolswere used for data collection (Socio-demographic and clinical data, Toronto Alexithymia Scale (TAS),Interpersonal Reactivity Index (IRI), and Social Anhedonia Scale (RSAS).Results, Conclusion&Recommendations: The present study revealed a significant positive correlation between alexithymiaand social anhedonia. However, empathy was negatively associated with both alexithymia and socialanhedonia. The results also provided evidence for a partial mediation effect of empathy in the relationshipbetween social anhedonia and alexithymia. This could be a stepping stone for developing morecomprehensive treatment plans not only improve social engagement but also foster empathy throughenhanced emotional awareness and expression, ultimately leading to better social functioning. This studysuggests that mental health nurses (MHNs) should prioritize assessing empathy, social anhedonia, andalexithymia in patients with schizophrenia. Also, psychiatric and mental health nurses should focus ontraining patients with schizophrenia to identify emotions, regulate them, and improvecommunication/social interaction to enhance empathy and reduce the impact of alexithymia on socialanhedonia. Moreover, building a strong relationship is crucial to provide effective psychoeducation forpatients and families on the importance of empathy in social interactions.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".