The Role of Relational Maintenance Behavior and Attachment Styles in Predicting Marital Commitment
Bibliographic record
Abstract
Commitment to both spouse and the institution of marriage appears to be important to the success of a marriage. The aim of the present study is to examine the role of relational maintenance behavior and attachment styles in predicting marital commitment. The statistical population has been consisted of all the couples who had middle school children in Tehran city; so, 372 married people (233 women and 139 men) have been selected by multiple cluster sampling. The Relational maintenance behavior measure (RMSM), Adult attachment questionnaire (AAQ) and personal commitment subscale have been considered as the data collection tools. The results have shown that there is a significant positive relationship between assurance subscale, openness, conflict management, share task, positivity, advice and secure attachment style and marital commitment, and there is a significant negative relationship between avoidant and ambivalent attachment styles and marital commitment. Also multiple regression analysis has shown that the four subscales of relational maintenance behavior (assurance, openness, conflict management and positivity) and attachment styles (secure, avoidant and ambivalent) can predict the marital commitment (p <0.05). According to these findings, it can be concluded that relational maintenance behaviors and attachment styles affect the marital commitment and commitment to marital relationship among couples can be increased by training relationship maintenance behaviors and providing necessary trainings related to attachment styles for parents.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".