Global Perspective on Marital Satisfaction
Bibliographic record
Abstract
Across the world, millions of couples get married each year. One of the strongest predictors of whether partners will remain in their relationship is their reported satisfaction. Marital satisfaction is commonly found to be a key predictor of both individual and relational well-being. Despite its importance in predicting relationship longevity, there are relatively few empirical research studies examining predictors of marital satisfaction outside of a Western context. To address this gap in the literature and complete the existing knowledge about global predictors of marital satisfaction, we used an open-access database of self-reported assessments of self-reported marital satisfaction with data from 7178 participants representing 33 different countries. The results showed that sex, age, religiosity, economic status, education, and cultural values were related, to various extents, to marital satisfaction across cultures. However, marriage duration, number of children, and gross domestic product (GDP) were not found to be predictors of marital satisfaction for countries represented in this sample. While 96% of the variance of marital satisfaction was attributed to individual factors, only 4% was associated with countries. Together, the results show that individual differences have a larger influence on marital satisfaction compared to the country of origin. Findings are discussed in terms of the advantages of conducting studies on large cross-cultural samples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".