Personal Agency and Social Supports to Manage Health Among Non-Hispanic Black and Hispanic Men With Diabetes
Bibliographic record
Abstract
The prevalence of type 2 diabetes (T2D) is increasing among non-Hispanic Black and Hispanic communities, especially among men who develop this chronic condition at earlier ages. Personal agency and social support are vital aspects to diabetes management. However, less is known about the relationship between these variables among men living with diabetes. The purposes of this study were to identify (1) levels of personal agency to manage health, (2) sources of social supports to manage health based on personal agency levels, and (3) factors associated with lower personal agency to manage health. Cross-sectional data from non-Hispanic Black ( n = 381) and Hispanic ( n = 292) men aged 40 years or older with T2D were collected using an internet-delivered questionnaire. Three binary logistic regression models were fitted to assess sociodemographics, health indicators, and support sources associated with weaker personal agency to manage health. About 68% of participants reported having the strongest personal agency relative to 32.1% reporting weaker personal agency. Men who relied more on their spouse/partner (odds ratio [OR] = 1.22, p = .025), coworkers (OR = 1.59, p = .008), or faith-based organizations (OR = 1.29, p = .029) for ongoing help/support to improve their health and manage health problems were more likely to have weaker personal agency. Conversely, men who relied more on their health care providers for ongoing help/support to improve their health and manage health problems were less likely to have weaker personal agency to manage health (OR = 0.74, p < .001). Findings suggest personal agency may influence men’s support needs to manage T2D, which may also be influenced by cultural, socioeconomics, and the composition of social networks.
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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) | 0.002 | 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".