Exploring Pathways to Caregiver Health: The Roles of Caregiver Burden, Familism, and Ethnicity
Bibliographic record
Abstract
ObjectivesThis study examines the associations of ethnicity, caregiver burden, familism, and physical and mental health among Mexican Americans (MAs) and non-Hispanic Whites (NHWs).MethodsWe recruited adults 65+ years with possible cognitive impairment (using the Montreal Cognitive Assessment score<26), and their caregivers living in Nueces County, Texas. We used weighted path analysis to test effects of ethnicity, familism, and caregiver burden on caregiver’s mental and physical health.Results516 caregivers and care-receivers participated. MA caregivers were younger, more likely female, and less educated compared to NHWs. Increased caregiver burden was associated with worse mental (B = −0.53; p < .001) and physical health (B = −0.15; p = .002). Familism was associated with lower burden (B = −0.14; p = .001). MA caregivers had stronger familism scores (B = 0.49; p < .001).DiscussionIncreased burden is associated with worse caregiver mental and physical health. MA caregivers had stronger familism resulting in better health. Findings can contribute to early identification, intervention, and coordination of services to help reduce caregiver burden.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".