Older caregivers’ depressive symptomatology over time: evidence from the Survey of Health, Ageing and Retirement in Europe
Bibliographic record
Abstract
The prevalence of informal caregiving is increasing as populations across the world age. Caregiving has been found to be associated with poor mental health outcomes including depressive symptoms. The purpose of this study is to examine the mean trajectory of depressive symptomatology in older caregivers in a large European sample over an eight-year period, the effects of time-varying and time-invariant covariates on this trajectory, and the mean trajectory of depressive symptomatology according to pattern of caregiving. The results suggest that depressive symptoms in the full sample of caregivers follow a nonlinear trajectory characterized by an initial decrease which decelerates over time. Caregiver status and depressive symptoms were significantly associated such that depressive symptoms increased as a function of caregiver status. The trajectory in caregivers who report intermittent or consecutive occasions of caregiving remained stable over time. Significant associations were found between sociodemographic, health and caregiving characteristics and the initial levels and rates of change of these trajectories. While these results point to the resilience of caregivers, they also highlight the factors that are related to caregivers' adaptation over time. This can help in identifying individuals who may require greater supports and, in turn, ensuring that caregivers preserve their well-being.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".