Changes in Family Structure and Increasing Care Gaps in the United States, 2015–2050
Bibliographic record
Abstract
Research on caregiving in the United States has not clearly identified the scope of the gap between care needed and care received and the changes implied by ongoing and anticipated shifts in family structure. This article examines the magnitude of contemporary gaps in care among older adults in the United States and how they are likely to evolve through 2050. We use data from the Health and Retirement Study (1998-2014) to estimate care gaps, operationalized as having difficulties with activities of daily living (ADLs) or instrumental activities of daily living (IADLs) but not receiving care. We also estimate variation in care gaps by family structure. Then, we use data from demographic microsimulation to explore the implications of demographic and family changes for the evolution of care gaps. We establish that care gaps are common, with 13% and 5% of adults aged 50 or older reporting a care gap for ADLs and IADLs, respectively. Next, we find that adults with neither partners nor children have the highest care gap rates. Last, we project that the number of older adults with care gaps will increase by more than 30% between 2015 and 2050-twice the rate of population growth. These results provide a benchmark for understanding the scope of the potential problem and considering how care gaps can be filled.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".