Subjective Well-Being Dynamics in Couples from the Australian Longitudinal Study of Aging
Bibliographic record
Abstract
BACKGROUND: There is growing evidence for spousal associations in late-life development among key functional domains. Spousal interrelations in subjective well-being (SWB) have primarily been discussed in the context of a model of 'transmission', an indicator of well-being. Typically, depression is used to mark this, but few studies have examined if such transmission can be found over the long term in older couples' SWB. OBJECTIVE: We aimed to determine whether longitudinal dyadic interrelations exist among older couples in the SWB domain, as indicated by morale. METHODS: We applied dynamic models to 11-year longitudinal data of 316 couples from the Australian Longitudinal Study of Aging (median age = 75 years at baseline) to explore whether the levels of SWB for one partner predict change in SWB for the other. RESULTS: Spousal interrelations emerged and were found to be gender-specific with wives predicting subsequent change among husbands, but not the reverse pattern of influence. Husbands whose wives reported higher initial SWB showed a relatively shallower decline over time relative to husbands whose wives reported lower initial SWB levels. These associations were robust after covarying for differences in age, education, health and marital characteristics (number of children and length of marriage). CONCLUSION: Our study is consistent with, and illustrates empirically that close relationships shape individual developmental outcomes. The findings suggest that wives play an important role in setting the affective tone in older couples. We discuss possible factors underlying such interrelations.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".