Mental health of patients with heart disease: analysis of alexithymia and family social support
Bibliographic record
Abstract
The present study was conducted to determine the relationship of alexithymia and family social support with the mental health of patients with cardiac condition. The correlational method was used in the study. 200 individuals were selected as sample, using consecutive sampling method, from patients with cardiac condition who visited Imam Ali Hospital in Kermanshah, Iran during March and April 2014. The data collection instruments were the Mental Health Inventory (GHQ-28), Alexithymia (TAS_20) and Perceived Social Support from Family (PSS-Fa). The data were analyzed using Pearson’s correlation coefficient and stepwise regression analysis. The results of the study showed that there was a positive association of alexithymia, the components of difficulty identifying feelings (DIF) and difficulty describing feelings (DDF) with mental health. Negative correlation was obtained between family social support and mental health. The results of the regression analysis showed that DIF and family social support had the ability to predict mental health. Considering the results, in treatment of cardiac diseases, it is recommended to provide psychological interventions, especially paying attention to the patients’ emotions and their family’s social support, in addition to doing medical actions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".