The Impact of Spouse’s Illness on Depressive Symptoms: The Roles of Spousal Caregiving and Marital Satisfaction
Bibliographic record
Abstract
OBJECTIVES: To examine (a) the relationship between own depressive symptoms and spouses' health condition changes among mid- and later-life couples and (b) the roles of marital relationship quality and spousal caregiving in this relationship. METHOD: Fixed-effect analyses were conducted using data from 3,055 couples aged 45 and older from Waves 1 (2006) to 4 (2012) of the Korean Longitudinal Study on Ageing. RESULTS: Spousal stroke was linked with higher depression symptoms. Spouses' onset of cancer was related to an increase in depressive symptoms for wives, but not for husbands. Spousal caregiving and marital satisfaction were significant moderators: Wives caring for spouses with cancer reported more depressive symptoms than those not providing care; husbands caring for spouses with lung disease reported more depressive symptoms than those not providing care. The associations between wives' heart disease, husbands' cancer diagnosis, and depressive symptoms were weaker for couples with higher marital satisfaction. DISCUSSION: The findings suggest variations across health condition types and gender. Relationship quality and caregiving are important contexts moderating the negative impact of spousal chronic illness on depression. Health care providers should be aware that spouses' health statuses are connected and that type of illness may affect the care context.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".