Does Marital Satisfaction Matter for Dyadic Associations Between Multimorbidity and Subjective Health Among Korean Married Couples in Middle and Later Life?
Bibliographic record
Abstract
The present study aims to investigate how marital satisfaction moderates the dyadic associations between multimorbidity and subjective health. Data were extracted from the Korea Longitudinal Study of Aging in 2016 and 2018. The sample was Korean married couples in middle and later life ( N = 780 couples with low marital satisfaction, N = 1,193 couples with high marital satisfaction). The independent variable was multimorbidity, measured by the number of chronic diseases per person. The dependent variables were subjective life expectancy and self-rated health to represent subjective health. Marital satisfaction was a binary moderator, dividing the sample into low and high marital satisfaction groups. We applied the Actor Partner Interdependency Model to examine actor and partner associations simultaneously and used multigroup analysis to test the moderating effects of marital satisfaction. The results showed that husbands’ multimorbidity was negatively associated with wives’ self-rated health among couples in both the low and high marital satisfaction groups. In couples with high marital satisfaction, wives’ multimorbidity was negatively associated with husbands’ self-rated health, but this was not true for couples with low marital satisfaction. Regarding actor effects, multimorbidity was associated with self-rated health in both marital satisfaction groups. The actor effect of multimorbidity on the subjective life expectancy was significant only among women with low marital satisfaction. These findings suggest that there are universal and gendered associations between multimorbidity and subjective health in couple relationships.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".