Explaining the Marital Adjustment Process in Iranian Women: A Grounded Theory Study
Bibliographic record
Abstract
Background: Marital adjustment has a positive effect on the physical and mental health of family members. The purpose of this study was to explain the process of marital adjustment in Iranian women. Methods: This research is based on qualitative data analysis using the grounded theory method. Theoretical and purposeful sampling was used for data collection. Sampling continued until theoretical saturation. A total of 15 women from Ahvaz participated in the semi-structured interview. Interviews were analyzed using the Strauss and Corbin comparative method. Results: By open coding, 17 primary codes, and axial coding, 9 major themes, related to marital adjustment were obtained. Marital adjustment: An incentive to maintain marital life were extracted as the central category influenced by contextual, causal, and confounding conditions. Contextual conditions include emotional bonding with parents and spouse. Causal conditions are an emphasis on self and relationship with another and how to express self in the relationship. Interfering conditions include a relationship with the main family, spouse, and social comparison. Strategies include defense mechanisms and empathy. The consequences of marital adjustment were also the sense of security and comfort. Conclusion: Marital adjustment can be considered in curing marital maladjustment and conflicts and help therapists to have a better understanding of marital dynamics.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".