The Mediator Role of Empathy and Emotional Intelligence in the Relationship between Alexithymia and Emotional Expression Styles
Bibliographic record
Abstract
Although there are studies on the effect of alexithymia on expressed emotion, emotion expression in people with alexithymia was not specifically examined. This study aimed to investigate the relationship between alexithymia and emotional expression styles and whether emotional intelligence and empathy mediate this relationship. A total of 254 teacher candidates were evaluated using the Toronto Alexithymia Scale, Empathy Quotient Scale, Trait Emotional Intelligence Questionnaire, and Emotional Expression Styles Inventory. We performed the analyses using structural equation models. Our results revealed that alexithymia indirectly affected the expression style of happiness and sadness emotions negatively and emotional intelligence and empathy played a mediator role in this effect of alexithymia. Also, an indirect positive relationship was identified between alexithymia and anger expression style, and it was found that this relationship was mediated by emotional intelligence. Accordingly, emotional expression styles were also related to other variables. The present study also determined that alexithymia level was significantly and negatively correlated with emotional intelligence and empathy levels. This is the first study to reveal that alexithymia is related to emotional expression styles and that emotional intelligence and empathy also have mediator roles in emotional expression styles. expression styles were also related to other variables.
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 | 0.001 | 0.000 |
| 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".