Bibliographic record
Abstract
Abstract Each year family caregivers provide care and services worth billions of dollars to support the needs of older Americans. Their support is invaluable to keep individuals in the community for as long as possible and to allow individuals to attain and maintain their highest practicable level of well-being. But what impact does caregiving have on one’s health? Does caregiver health decline with the assumption of caregiving duties? Did caregiver health change during the pandemic? If so, how and what factors are associated with declines in caregiver health? To answer these questions, an exploratory survey was conducted among a convenience sample of 195 family caregiver. Almost a third of those sampled reported excellent or very good health, while 44% reported good health, and almost a quarter reported poor or fair health. Forty-eight percent reported their health had declined since they assumed caregiving duties and 29% reported their health had declined during the pandemic. Employed caregivers and those experiencing less depression/anxiety reported better health. Those experiencing a decline in health with caregiving were more likely to be female, not employed, experienced more stress and more depression/anxiety. Those experiencing a decline in health during the pandemic reported less spirituality, greater attachment related avoidance, and greater depression/anxiety. Findings from this research can be used to inform future research on the effect of the pandemic on family caregiving and to plan interventions to protect caregiver health as they provide vital services to maintain individuals in the community for as long as possible.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".