Gender, Illness-Related Diabetes Social Support, and Glycemic Control Among Middle-Aged and Older Adults
Bibliographic record
Abstract
OBJECTIVES: This study examined whether the association between illness-related diabetes social support (IRDSS) and glycemic control among middle-aged and older adults is different for men and women. METHOD: This cross-sectional analysis included 914 adults with diabetes who completed the Health and Retirement Study's 2003 Mail Survey on Diabetes. IRDSS is a composite score of 8 diabetes self-care measures. Hemoglobin A1c levels were obtained to measure good glycemic control (<8.0%). Gender-stratified multivariate log-binomial regression models were used to estimate prevalence ratios and examine the association between IRDSS and glycemic control after controlling for sociodemographic, lifestyle, and clinical characteristics. RESULTS: The prevalence of good glycemic control was 48.9% among women and 51.1% among men. Mean composite IRDSS scores did not differ by gender. Among women, composite IRDSS was associated with adequate glycemic control (prevalence ratio: 1.06; 95% confidence interval: 1.02, 1.08), and all individual components of IRDSS, with the exception of keeping appointments, were positively associated with adequate glycemic control. No significant associations were observed in men for composite or individual components of IRDSS. DISCUSSION: Determining the gender-specific impact derived from IRDSS is a worthwhile approach to highlighting factors that differentially predict optimal glycemic control among middle-aged and older adults.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".