Formal Assistance Among Dutch Older Adults: An Examination of the Gendered Nature of Marital History
Bibliographic record
Abstract
ABSTRACT Drawing from life course theory, this article examines gender differences in formal assistance among functionally dependent Dutch older persons within five distinct marital history groups – first-married, never-married, divorced (not remarried), widowed (not remarried) and the remarried. Hierarchical logistic regression analyses are performed for each of the marital history groups to test hypotheses regarding the interrelationships among gender and three sets of variables: 1) measures of age and functional health; 2) measures of socio-economic status; and 3) measures of the social network. The results indicate gendered patterns of formal help use among the first-married, never-married and widowed. Consistent with other studies, older first-married women are approximately three times more likely to receive formal help than are men, a difference that remains robust after statistically controlling for other factors, including frailty of spouse. However, we also find that never-married women are about one-third as likely to use formal help than are never-married men, which may be reflective of different preferences regarding formal service use. Among the widowed, we find that men with poorer functional health are more likely to receive formal help than are their female counterparts, suggesting contrasting patterns of help-seeking behaviour and social vulnerability. Additional differences are observed among the marital history groups in terms of the other independent variables, which are also interpreted from a life course perspective.
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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 | 0.001 | 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".