Predicting Marital Conflicts based on Alexithymia with the Mediating Role of Emotion Regulation
Bibliographic record
Abstract
Research has consistently highlighted the association between alexithymia and various challenges in personal and marital life. Consequently, the primary objective of this study was to predict marital conflicts with Alexithymia as the predictor, while considering the mediating role of emotion regulation. This study adopted a descriptive correlational design, employing structural equation modeling to examine the relationships between the variables. The study's statistical population encompassed all couples seeking assistance at counseling centers in Shiraz during the winter of 2021, particularly those grappling with marital conflict issues. The sample size consisted of 394 individuals, selected through cluster random sampling. Data collection involved the utilization of several questionnaires, including the Sanai Marital Conflict Questionnaire, the Toronto Alexithymia Questionnaire and Garnefski Cognitive Emotion Regulation Questionnaire. The results of this investigation demonstrated that the proposed model exhibited a favorable fit with the data, as indicated by various fit indices. Furthermore, both direct and indirect coefficients between alexithymia and marital conflicts were deemed statistically significant (p < 0.05). In essence, this implies that alexithymia can contribute to the escalation of marital conflicts, and emotion regulation plays a mediating role in this relationship. Overall, the findings of this study have valuable implications for family counselors and psychologists, offering insights into the development of effective interventions aimed at mitigating marital conflicts among couples.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".