Dementia caregiver burden and its toll on caregiver health
Bibliographic record
Abstract
Abstract Background In 2019, more than 16 million U.S. family members and friends provided 18.6 billion hours of unpaid care to people with cognitive impairment. Mexican Americans (MAs) make up two thirds of the Latinx population and research shows that MA caregivers report more caregiver burden compared to non‐Hispanic Whites (NHW). The purpose of this study is to examine the underlying mechanisms influencing caregiver burden on the physical and mental health of caregivers by ethnicity and familism. Method We recruited adults 65 and older with cognitive impairment (Montreal Cognitive Assessment score>25), and their caregivers living in Nueces County, Texas. Caregiver burden was measured using the Zarit Burden Interview. Familism was measured using a 18‐item scale. Caregiver’s mental and physical health were derived from the 36‐Item Short Form Survey. Covariates included caregiver’s age, sex, ethnicity, education and care recipient’s cognitive function and physical impairment, including the ability to eat, dress or go to the toilet without assistance. We used weighted path analysis to test effects of ethnicity, familism and caregiver burden on caregiver’s mental and physical health. Covariates with p‐values>0.15 were excluded to attain a parsimonious reduced model. Result We included data from 516 caregivers and those they cared for. MA caregivers were younger, female, had less education and scored higher on the familism scale compared to NHW (Table 1). Figure 1 includes results from our reduced weighted path model which presented a good model fit. Increased caregiver burden was associated with worse mental (B = ‐0.53;p<0.001) and physical health (B = ‐0.14;p = 0.003). Familism had indirect positive effects on mental and physical health outcomes mediated by lowering caregiver burden (B = ‐0.15;p = 0.001). Being a MA caregiver was associated with a stronger level of familism (B = 0.49;p<0.001). Findings indicate that MA caregivers have better physical and mental health due to reduced caregiver burden because of stronger family relations. Conclusion Results show that increased caregiver burden is associated with worse caregiver’s mental and physical health. However, among MA, familism can be a protective factor against caregiver burden. Findings can contribute to early identification, intervention and coordination of services to help reduce caregiver burden, particularly in the growing MA informal caregiver population.
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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 | 0.010 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".