Comparison early maladaptive schemas, alexithymia and happiness in women with marital conflicts with normal women
Bibliographic record
Abstract
Background and Aims Couple conflict is an inevitable event, because no two people are similar in interests, feelings, choices and behaviors. At the same time, marital conflict can interfere with interpersonal relationships and cause family problems. The main goal of the present study was to determine the difference between the early maladaptive schemas and emotional collapse and happiness in women with marital conflicts and women without conflict in Rudsar City, Iran. Methods The present study was an analytical descriptive comparative study. Using Krejci Morgan‘s Table, 103 women with marital conflict covered by the centers of the Rudsar Relief Committee in 2015 who were referred to the family courts of this city were selected by convenience sampling and compared with another 103 women without marital conflict. The data were collected through inferential marital conflict questionnaire, alexithymia scale of Toronto, happiness scale questionnaire, and initial inappropriate schema questionnaire. Data were analyzed using Lavin, Box’s M and multivariate analysis of variance in SPSS version 18. Results The results showed that the variables between the two groups alexithymia, happiness and there schema (P<0.01). Conclusion According to the results of the research, happiness can reduce marital conflict in couples . Also, due to the difference in study groups regarding the emotional variables and inappropriate schemas, it is possible to reduce marital conflicts by educating couples in this regard.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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".