DEPENDS ON WHO YOU ASK: DISCORDANCE IN REPORTING SPOUSAL CARE BETWEEN WOMEN AND MEN ACROSS EUROPEAN WELFARE STATES
Bibliographic record
Abstract
Abstract Current research on determinants, prevalence, and effects of informal care on health assumes that survey instruments can appropriately identify informal care providers. We aimed to investigate systematic differences across Europe in reporting spousal care between caregivers and cared-for persons and their possible effects on associations between informal care and physical and mental health indicators. Using information on care provided/received from the Survey on Health, Ageing and Retirement in Europe (SHARE), we estimated prevalence rates of spousal care and discordance between caregivers and cared-for persons in the reporting of care among caregiving dyads. We used multinomial regressions to estimate differences in reporting spousal care across European countries and then constructed multivariate logistic regression models to evaluate the effect of discordance in reporting informal care on the correlation between care and poor self-rated health and depression. Results showed that only 53.9% of dyads report care confirmed by both spouses. Multinomial regressions showed that agreement on care provided/received was more common when women are caregivers, while men were more likely to underreport providing/receiving care. There was no effect on the association of care with SRH regardless of who is asked to identify the care providers, while the magnitude and direction of the association between depression symptoms and care varies according to the choice of respondent. Possible explanations for the discordance include social norms dictating gender roles and acceptability of men as care providers. Relying on self-identification of care providers as most surveys do has the potential to underestimate their numbers.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".