Challenges to Social Connection Among Black Men with Chronic Conditions: Examination of Structural, Functional, and Quality Domains
Bibliographic record
Abstract
Objectives: Limited social connection places individuals at greater risk for chronic conditions; however, there is limited research examining the association between chronic conditions and barriers to disease self-management on social connections. Our study addresses this gap in the empirical literature by examining these issues among Black men aged 40+ years with 1 or more chronic conditions. Methods: Data came from a national sample of 1200 Black men. We conducted 3 multivariate logistic regression models examining social connection domains (did not have people to call for help, felt isolated from others, and were not content with relationships/friendships) on 3 independent variables: the number of chronic health conditions, cut down or skipped social activities because of health problems, and self-reported barriers to disease self-management. Sociodemographic covariates for all regression models included age, education, partner status, and annual household income. Results: Men were aged 56.7 (±9.7) years and self-reported 4.0 (±2.9) chronic conditions. Approximately 1 in 4 participants reported that they did not have enough people to call for help (25.2%), felt isolated from others (26.0%), and were not content with friendships/relationships (23.8%). Across multivariate models, men who reported more barriers to disease self-management were significantly more likely to report a social connection domain challenge. The number of chronic conditions and cutting down or skipping social activities because of health problems were also associated with a greater likelihood of social connection challenges. Conclusions: Efforts to improve the self-management of illness symptomology may mitigate challenges to social connection among middle-aged and older Black men.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".