Investigating the Relationship between Alexithymia and Type D personality, Mediated by Perceived Stress on Blood Pressure of Heart Patients
Bibliographic record
Abstract
Aim: The present study aimed to determine the relationship between Alexithymia and type D personality with the mediation of stress on the blood pressure of heart patients. Method: The research was descriptive and correlational and its statistical population consisted of heart patients who visited Imam Reza Hospital in Mashhad in the winter of 2014. Sampling was done by a convenience method and 200 out of 623 patients, who visited the hospital, were selected and responded to Perceived Stress Questionnaire by Cohen, Kamarck, & Mermelstein (1983), type D personality questionnaire, and the Persian version of Toronto Alexithymia Scale by Besharat (2007). We analyzed the results using the path analysis model and correlation coefficient. Results: The results indicated that type D personality and Alexithymia, mediated by perceived stress, could predict hypertension. The path analysis pattern was approved, hence, the pathway of personality D to blood pressure with a standard coefficient of 0.24, and the pathway of stress to blood pressure with a standard coefficient of 0.22 were significant but the pathway of personality D to stress with an effect size of 0.11 and the pathway of Alexithymia to stress with an effect size of 0.02 were not significant. It should be noted that there was no causal effect of personality on stress, Alexithymia on blood pressure, and Alexithymia on stress in the structural pattern, and there was a correlation. Conclusion: The psychological components such as type D personality, Alexithymia, and perceived stress could be effective in increasing chronic physical diseases such as hypertension and other heart diseases.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".