Investigation the structural model of the relationship between cognitive emotion regulation and defensive lifestyles with quality of life mediated by Alexithymia in patients with hypertension
Bibliographic record
Abstract
Background: High blood pressure is one of the most common and important factors threatening individuals' mental health and quality of life.Accordingly, the study of important psychological factors such as cognitive regulation of emotion and defense styles used by patients with high blood pressure, and the impact of these variables on their quality of life can help them improve their psychological state.Aims: The purpose of this study was to investigate the structural model of the relationship between cognitive emotion regulation and defensive lifestyles with quality of life mediated by Alexithymia in patients with hypertension.Methods: The present study is descriptive and of correlations and structural equations type.The statistical population includes 700 patients with hypertension who referred to Shiraz Heart Hospital in the spring and summer of 1398.480 patients were selected from among them.The research instruments included the Cognitive Emotion Regulation Questionnaire of Garnefski et al. (2007), the Defense Styles Questionnaire of et al. (1993), Quality of Life Questionnaire of the World Health Organization (1996) and the Toronto Alexithymia Scale-20 Questionnaire by Tyler et al. (1994).Data were analyzed by Kolmogorov-Smirnov test, linear regression and structural equation modeling using SPSS 25 and LISREL 8/8 software.Results: The relationship between emotional cognitive regulation variables and defense styles with quality of life variable mediated by alexithymia in patients with hypertension is significant (P< 0.001).Conclusion: Applying cognitive emotion regulation strategies and defensive styles to overcome alexithymia by patients with high blood pressure can affect the quality of life of these 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".