Impact of Distress Reduction on Behavioral Correlates and A1C in African American Women with Uncontrolled Type 2 Diabetes: Results from EMPOWER
Bibliographic record
Abstract
OBJECTIVE: Symptoms of emotional distress related to diabetes have been associated with inadequate self-care behaviors, medication non-adherence, and poor glycemic control that may predispose patients to premature death. African American women, in whom diabetes is more common and social support is often insufficient, may be at particularly high risk. The objective of this study was to examine the impact of lowering diabetes-related emotional distress on glycemic control and associated behavioral correlates in rural African American women with uncontrolled type 2 diabetes (T2D). DESIGN: Post-hoc analysis of prospective, randomized, controlled trial. SETTING: Rural communities in the southeastern United States. PATIENTS: 129 rural middle-aged African American women with uncontrolled type 2 diabetes (T2D)(A1C ≥ 7.0). PRIMARY INDEPENDENT VARIABLE: Diabetes-related distress. MAIN OUTCOME MEASURES: Changes from baseline to 12-month follow-up in diabetes-related distress, and associated changes in medication adherence, self-care activities, self-efficacy, and glycemic control (A1C). RESULTS: Patients with a reduction in diabetes-related distress (n=79) had significantly greater improvement in A1C, medication adherence, self-care activities, and self-efficacy compared with those in whom diabetes distress worsened or was unchanged (n=50). Changes in distress were also significantly and inversely correlated with improvements in medication adherence, self-care activities, and self-efficacy. CONCLUSIONS: Among rural African American women, reductions in diabetes-related distress may be associated with lower A1C and improvements in self-efficacy, self-care behaviors, and medication adherence.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".