Health Care Engagement in Disease Prevention and Management: Factors Influencing Chronic Disease Program Referral Adherence Among Non-Hispanic Black and Hispanic Men With Chronic Conditions
Bibliographic record
Abstract
This study aimed to identify factors associated with being referred to an evidence-based disease prevention and management program by a health care provider and adherence to such referrals by non-Hispanic Black and Hispanic men. Utilizing a cross-sectional design, data were collected via an internet-based questionnaire from a national sample of 1,679 non-Hispanic Black and Hispanic men ages 40 years and older with one or more chronic diseases. A 105-item survey assessed program referral and attendance, chronic conditions and medications, disease symptoms, support, communication during physician visit, health care frustrations, disease self-management efficacy, barriers to self-care, helpfulness of learning from others for self-care, and sociodemographics. Binary logistic regression models were fitted to assess factors associated with referrals to a disease prevention and management program and attendance. Results indicated that approximately 23% of participants were referred to a program, and 19.2% reported attendance. Factors associated with being referred to and attending a program included being younger, having more chronic conditions, taking more medications daily, having higher pain scores, reporting more health care frustrations, and reporting better communication with physicians during visits. Men referred to attend a chronic disease program by a health care provider were 16.86 times more likely to attend a chronic disease program ( p < .001). These findings suggest the importance of health care engagement for non-clinical disease prevention and management programs, particularly among non-Hispanic Black and Hispanic men with complex disease profiles.
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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 | 0.004 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".