Associations of depressive symptoms, social engagement and support, and lifestyle behaviors among non-Hispanic black and Hispanic men with chronic conditions in the United States
Bibliographic record
Abstract
Introduction Self-management of depressive symptoms is influenced by co-morbidity, social support, and health-related behaviors. Men are less likely to discuss depressive moods and seek healthcare. This study examines factors associated with depressive symptoms among non-Hispanic Black and Hispanic men ages ≥40 years with ≥1 chronic condition in the U. S. Methods Data from 1,907 non-Hispanic Black (n = 1,117) and Hispanic (n = 790) males with chronic conditions were analyzed using logistic regression to assess depressive symptoms, identified as a Patient Health Questionnaire-2 score ≥3. One model was fitted for all men, then separate models were fitted for non-Hispanic Black and Hispanic men, respectively. The models adjusted for sociodemographic, disease characteristics, health status, social engagement and support, and lifestyle behaviors. Results In the full model, Hispanic men (OR = 1.39, p = 0.017) and those taking more medications (OR = 1.10, p = 0.010) were more likely to have depressive symptoms. Social disconnection (OR = 1.65, p < 0.001), reliance on others for health management (OR = 1.04, p < 0.001), limited activity due to health (OR = 3.15, p < 0.001), self-care barriers (OR = 1.16, p < 0.001), healthcare frustration (OR = 1.13, p < 0.001), prolonged sitting (OR = 1.01, p = 0.030), and tobacco use (OR = 1.56, p = 0.002) increased likelihood of depressive symptoms. Common and unique factors associated with depressive symptoms were identified in models for non-Hispanic Black and Hispanic men, respectively. Conclusion Findings highlight the dynamic interplay between depressive symptoms, social engagement, and lifestyle behaviors among non-Hispanic Black and Hispanic men with complex disease profiles. Efforts are needed to address depressive symptomatology through self-managing conditions, strengthening supportive networks, and alleviating burdens associated with healthcare interactions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".