Depends on whom you ask: Discordance in reporting spousal care between older women and men across European welfare states
Bibliographic record
Abstract
PURPOSE: We aim to investigate systematic differences in reporting spousal care between caregivers and cared-for persons and their possible effects for the analysis of care regimes and correlation of care with health. MATERIALS AND METHODS: Using information on care provided/received from the Survey on Health, Ageing and Retirement in Europe (SHARE), we estimate the prevalence of spousal care and discordance between caregivers and cared-for persons in the reporting of care among caregiving dyads. Multinomial regressions are used to estimate systematic differences in reporting spousal care. We then use multivariable logistic regressions to assess the association between discordance in reporting informal care and carer's self-rated health (SRH) and depression using the EURO-D scale. RESULTS: Only 53.9 % of dyads report care that is confirmed by both spouses. Multinomial regressions show that agreement on care being provided/received is more common when women are caregivers, while men are likely to underreport when providing or receiving personal care. Prevalence of spousal care across care regimes is sensitive to who reports care. There is no effect on the association of care with SRH regardless of who identifies the carer, while the magnitude and statistical significance of the association between depression symptoms and care varies according to the choice of respondent. CONCLUSIONS: Informal care may be understated across Europe when relying solely on carer self-identification through description of tasks in surveys. From a policy standpoint, relying on self-identification of carers to access support or social benefits may potentially reduce the take-up of such benefits or support.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".