Cohort Trajectories by Age and Gender for Informal Caregiving in Europe Adjusted for Sociodemographic Changes, 2004 and 2015
Bibliographic record
Abstract
OBJECTIVES: We present a dynamic view of gender patterns in informal caregiving across Europe in a context of sociodemographic transformations. We aim to answer the following research questions: (a) has the gender gap in informal caregiving changed; (b) if so, is this due to changes among women and/or men; and (c) has the gender care gap changed differently across care regimes? METHODS: Multilevel growth curve models are applied to gendered trajectories of informal caregiving of a panel sample of 50+ Europeans, grouped into 5-year cohorts and followed across 5 waves of the Survey of Health, Ageing and Retirement in Europe survey, stratified by sex and adjusted for several covariates. RESULTS: For men in cohorts born more recently, there is a decrease in the prevalence of informal care outside the household, whereas cohort trajectories for women are mostly stable. Prevalence of care inside the household has increased for later-born cohorts for all without discernible changes to the gender care gap. Gender care gaps overall widened among later-born cohorts in the Continental cluster, whereas they remained constant in Southern Europe, and narrowed in the Nordic cluster. DISCUSSION: We discuss the cohort effects found in the context of gender differences in employment and care around retirement age, as well as possible demographic explanations for these. The shift from care outside to inside the household, where it mostly consists of spousal care, may require different policies to support carers, whose age profile and possible care burden seem to be increasing.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".