BARRIERS TO DISEASE SELF-CARE AMONG OLDER NON-HISPANIC BLACK AND HISPANIC MEN WITH CHRONIC CONDITIONS
Bibliographic record
Abstract
Abstract Disease self-care is multi-faceted and influenced by physical, mental, social, and environmental factors. Because males traditionally underutilize preventive healthcare services, self-care is increasingly important to manage disease symptomatology and slow disease progression. This study examines factors associated with barriers to disease self-care among Hispanic and non-Hispanic Black men ages ≥65 years with ≥1 chronic condition. Data were analyzed from a national sample of 470 Hispanic (43.2%) and non-Hispanic Black (56.8%) men and collected with an internet-delivered questionnaire. The 5-item Barriers to Self-Care Scale served as the dependent variable (range=5-20). A least squares regression model was fitted to identify factors associated with barriers to disease self-care. The model adjusted for sociodemographics, disease characteristics, health status, and social engagement and support. On average, participants were age 70.1 (±4.5) years and reported 3.9 (±2.6) chronic conditions. Men with higher body mass index (β=0.13, P=0.001), more depressive symptomatology (β=0.09, P=0.046), and lower self-rated quality of life (β=-0.23, P< 0.001) reported higher barriers to disease self-care. Further, men with poorer access to healthcare (β=-0.17, P< 0.001) and higher healthcare frustrations (β=0.17, P< 0.001) reported higher barriers to disease self-care. Barriers to self-care were positively associated with being more socially disconnected (β=0.09, P=0.049) and higher reliance on others to manage health problems (β=0.14, P< 0.001). Findings reinforce the importance of mental health, healthcare access, and social support for disease self-management among Hispanic and non-Hispanic Black men. Efforts are needed in clinical and community-based settings to reduce self-care barriers through culturally appropriate self-management education, interventions, and services.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".