The Role of Alexithymia and Dysfunctional Reactions in Predicting Marital Intimacy
Bibliographic record
Abstract
Objective: This study aims to investigate the relationship between alexithymia and dysfunctional reactions with marital intimacy among married men and women in Mashhad City, Iran. Methods: The data collection tools in this study included the Persian version of the Toronto alexithymia scale-20, the dysfunctional attitude scale (Weissman and Beck), and the intimacy scale (Walker and Thompson). The statistical population included all married men and women who lived in Mashhad City in the second half of 2019. The sample of this study consisted of 171 married individuals (100 women and 71 men) who were selected via the convenience sampling method. Results: he results indicated a negative and significant relationship between marital intimacy and alexithymia (P<0.05, β=-25.258) and dysfunctional reactions (P<0.01, β=-0.0318). Alexithymia accounted for about 6% and dysfunctional reactions for about 10% of the dispersion of marital intimacy. Also, a positive and significant correlation existed between alexithymia and dysfunctional reactions (P<0.01, r=0.553) among married couples. In addition, alexithymia and dysfunctional reactions could be a predictor of marital intimacy. Conclusion: Paying attention to variables, such as alexithymia and dysfunctional reactions can ensure marital intimacy. The results of this study have implications for improving marital intimacy and its positive outcomes in families.
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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 | 0.001 | 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".