Widowhood, Age Heterogamy, and Health: The Role of Selection, Marital Quality, and Health Behaviors
Bibliographic record
Abstract
OBJECTIVE: Although the impact of widowhood on the surviving spouse's health has been widely documented, there is little empirical research examining whether certain spousal choice decisions and marital sorting patterns predispose individuals to be more vulnerable to the adverse consequences of widowhood for health. DESIGN AND METHOD: We use data from the Wisconsin Longitudinal Study and employ ordinary least squares models to (a) document variations in mental and physical health between married and widowed persons, (b) determine whether widowed persons in age heterogamous unions are especially vulnerable to the adverse consequences of widowhood, and (c) investigate to what extent differential selection, marital quality, and health practices account for health disparities by marital status and the spousal age gap. RESULTS: Widowed persons, especially those in age heterogamous unions, have worse mental health than married persons, but they do not seem to be more disadvantaged in terms of physical health. Differential selection, marital quality, and health behaviors partly account for some of the health disparities by marital status and spousal age gap. DISCUSSION: Our findings suggest that marrying a spouse who is very dissimilar in age may enhance one's vulnerability to the adverse consequences of widowhood for health.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".