The Relationship between Alexithymia and Emotional Maturity with the Mediation of Attachment Styles in Cardiovascular Patients
Bibliographic record
Abstract
Introduction: Psychological factors might play a role in the onset and exacerbation of cardiovascular disease. This study aimed to determine the relationship between Alexithymia and Emotional Maturity with the mediation of Attachment Styles among cardiovascular patients in Zanjan, Iran.Methods: The present study has a descriptive-correlational design. The statistical population of this study encompassed a range of 670 patients with cardiovascular diseases who were referred to the Cardiology Clinic of Ayatollah Mousavi Hospital in Zanjan from March and August 2019. The sample size was estimated to be 244 people using Cochran formula. Data collection tools included Hazen and Shaver’s standard adult attachment style questionnaire, Toronto’s ataxia, and Singh and Bhargava questionnaires. The Pearson correlation coefficient and multiple regression analyses were done using SPSS 24 software.Results: There was a positive correlation between alexithymia with emotional instability (r=0.14, P<0.01), personality decay (r=0.36, P<0.01), and emotional maturity (r=0.38, P<0.01). The results of multiple regression analysis showed that emotional instability (P=0.009), personality decay (P=0.016), and emotional immaturity (P=0.009) significantly predicted secure style. Additionally, difficulty in describing emotions significantly predicted avoidant style (P=0.034). Emotional maturity (P=0.006) and difficulty recognizing emotions (P=0.009) also significantly predicted ambivalent style.Conclusion: Personality traits and emotional alexithymia can be used as indicators for predicting attachment styles and social behaviors, and specifically, lack of emotional stability, personality disintegration, and emotional alexithymia can predict secure and ambivalent styles to some extent. Also, difficulty in describing emotions and emotional immaturity have the ability to predict the avoidant style. These results can help to better understand individual behaviors and design solutions to improve behavior.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".