Back to Work? Not Everyone. Examining the Longitudinal Relationships Between Informal Caregiving and Paid Work After Formal Retirement
Bibliographic record
Abstract
OBJECTIVES: Research on unretirement (retirees who re-enter the workforce) is burgeoning. However, no longitudinal study has examined how informal care relates to unretirement. Utilizing role theory, this study aims to explore the heterogeneity of informal care responsibilities in retirement and to examine how informal care informs re-entering the workforce in later life. METHOD: Data were drawn from the Health and Retirement Study of fully retired individuals aged 62 years and older in 1998 (n = 8,334) and followed to 2008. Informal care responsibilities included helping a spouse/partner with activities of daily living (ADLs) or instrumental activities of daily living (IADLs); helping parent(s) or parent-in-law(s) with ADLs or IADLs; and single or co-occurrence of care roles. Covariates included economic and social factors. Cox proportional hazard models were utilized. RESULTS: When compared with noncaregivers, helping a spouse with ADLs or IADLs reduced the odds of returning-to-work in the subsequent wave by 78% and 55%, respectively (hazard ratio [HR]: 0.22, confidence interval [CI]: 0.06-0.87; HR: 0.45, CI: 0.21-0.97). There was no statistical difference to returning-to-work between noncaregivers and helping parents with ADLs/IADLs or multiple caregiving responsibilities. DISCUSSION: Role theory provided a useful framework to understand the relationships of informal care and unretirement. Aspects of role strain emerged, where, spousal caregivers were less likely to come out of retirement. Spousal caregivers may face challenges to working longer, and subsequently, opportunities to bolster their retirement security are diminished. Research and policy implications are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".