Influences of Family Structure and Intimacy on Emotional Divorce: A Cross-Sectional Analysis
Bibliographic record
Abstract
Objective: This study aims to explore the predictive roles of family structure and intimacy in emotional divorce among married individuals, highlighting the nuanced interplay between these factors and marital disengagement. Methods and Materials: Employing a cross-sectional design, 300 married participants were recruited through convenience sampling from counseling centers and online community forums, with an inclusion criterion of being in a marriage for at least two years. The Family Structure Scale and Personal Assessment of Intimacy in Relationships Scale, alongside a custom questionnaire for emotional divorce, served as the primary measurement tools. Data analysis involved multiple regression techniques to assess predictive relationships, adhering to statistical assumptions of linearity, independence, homoscedasticity, and normality. Findings: Results indicated significant predictive relationships between both family structure and intimacy with emotional divorce, suggesting that variations in these factors could significantly influence the likelihood of emotional disengagement within marriage. Detailed analysis underscored the importance of considering these elements in understanding marital dynamics. Conclusion: The study underscores the complex relationship between family structure, intimacy, and emotional divorce, advocating for targeted interventions to enhance marital cohesion. Future research should explore these dynamics longitudinally and across diverse populations to further elucidate these relationships.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".