CAREGIVER STRAIN AMONG AFRICAN AMERICAN AND HISPANIC MALE CAREGIVERS WITH CHRONIC CONDITIONS
Bibliographic record
Abstract
Abstract Caregiving strain often stems from caregivers’ unmet needs and is a risk factor for physical and psychological ill-health. This study aims to identify factors associated with caregiver strain among middle-aged and older African American and Hispanic male caregivers living with one or more chronic conditions. Data were collected from 431 male caregivers using a web-based survey (55% African American, 45% Hispanic). Linear regression models were fitted to assess factors associated with caregiver strain, which was measured using caregiving difficulty items from Behavioral Risk Factor Surveillance System. On average, participants were age 54.9(±9.51) years, they self-reported chronic conditions were 3.74(±2.62), and their caregiver strain was 14.7(±7.30). Among African American caregivers, higher caregiver strain was positively associated with living with children below age 18 (β=0.14, P=0.045) and feelings of social disconnectedness (β=0.16, P=0.018) and depression (β=0.15, P=0.035). Conversely, caregiver strain was negatively associated with having insurance coverage (β=-1.34, P=0.028) and disease self-management efficacy (β=-2.26, P=< 0.001. Among Hispanic caregivers, higher caregiver strain was negatively associated with age (β=-0.28, P=< 0.001) and positively associated with feelings of social disconnectedness (β=0.16, P=0.041). Findings suggest African American and Hispanic males with chronic conditions have differing caregiving experiences. Compared to Hispanic men, contributors to caregiving strain among African American men were multifaceted and associated with financial resources, household dynamics, mental health, and the ability to self-manage their chronic conditions. While bolstering social connectedness may offset caregiver strain, tailored mental health and disease-management programming are needed to meet the specific needs of African American and Hispanic male caregivers.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".