The Relationship of Interpersonal Conflict Handling Styles and Marital Conflicts Among Iranian Divorcing Couples
Bibliographic record
Abstract
BACKGROUND: Various research studies have suggested that among other variables that couples remain married if they successfully manage their interactions (marital communication based on acceptance of individual differences, problem solving skills, forgiveness, collaborative decision making, empathy and active listening) and constructively manage conflict. PURPOSE: The study was aimed at examining the relation of conflict handling styles and marital conflicts among divorcing couples. METHODS: As a descriptive -comparative study 60 couples out of 440 couples referred to the Crisis Intervention Center of the Isfahan Well-being Organization have selected. The tools implemented were Marital Conflicts (Barati & Sanaei, 1996) and Interpersonal Conflict Handling Styles Questionnaires (Thomas-Kilman, 1975). Their total reliabilities were, respectively, 0.74 and 0.87. RESULTS: Findings showed that there are no significant differences among their conflict handling styles and marital conflicts. Also, there was positive correlation between avoidance and competition styles and negative one between compromise, accommodation, and cooperation styles with marital conflicts. That is, these styles reduced couples' conflicts. Finally, wives had tendency to apply accommodation style and husbands tended to use accommodation and cooperation styles to handle their conflicts. CONCLUSIONS: It is suggested to be studied couples' views toward their own styles to handle marital conflicts and holding training courses to orient couples with advantages and disadvantages of marital conflict handling styles.
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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 | 0.005 |
| 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 | 0.001 | 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".