Comparing alexithymia and emotional expressiveness in patients with coronary heart disease and healthy people
Bibliographic record
Abstract
Background: Previous studies have reported the high rate of alexithymia in medical and psychological disorders along with the role of emotional expressiveness practices in terms of different aspects of health. The aim of this study was to compare the alexithymia and emotional expressiveness in patients with coronary heart disease and healthy people. Materials and Methods: In this ex post facto research the participants (n=35) with coronary heart disease (CHD) referred to Chamran and Sina Heart Center in Isfahan (2014) and healthy individuals (n=35) selected based on available sampling method were compared. Toronto Alexithymia Scale (TAS) and Emotional Expressiveness Scale (EEQ) were used to evaluate the participants. Results: The results were indicated a difference in alexithymia and emotional expressiveness in patients with CHD compared to those of the healthy ones (P<0.05). Patients with CHD were significantly different from healthy individuals in terms of two out of three aspects of Alexithymia: difficulty in describing feelings and identifying feelings (P<0.05). The two groups were also significantly different from each other with respect to positive emotion expression and intimacy expression, considered to be two of three aspects of emotional expressiveness (P<0.05). Conclusion: Alexithymia and emotional expressiveness are important in better understanding of the psychopathology of patients with CHD. Therefore, it appears necessary to pay attention to this item as a factor influencing the treatment process of such patients.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".