Community Interventions to Improve Glycemic Control in African Americans with Type 2 Diabetes: A Systemic Review
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to conduct a systematic review of published community interventions to evaluate different components of community interventions and their ability to positively impact glycemic control in African Americans with T2DM. METHODS: Medline, PsychInfo, and CINAHL were searched for potentially eligible studies published from January 2000 through January 2012. The following inclusion criteria were established for publications: (1) describe a community intervention, not prevention; (2) specifically indicate, in data analysis and results, the impact of the community intervention on African American adults, 18 years and older; (3) measure glycemic control (HbA1C) as an outcome measure; and (4) involve patients in a community setting, which excludes hospitals and hospital clinics. RESULTS: Thirteen studies out of 9,233 articles identified in the search met the predetermined inclusion criteria. There were 5 randomized control trials and 3 reported improved glycemic control in the intervention group compared to the control group at the completion of the study. Of the 8 studies that were not randomized control trials, 6 showed a statistically significant change in HbA1C. CONCLUSION: In general, the community interventions assessed led to significant reductions in HbA1C in African Americans with type 2 diabetes. Community health workers did not have a greater impact on glycemic control in this sample. The findings of this study provides insight for designing community-based interventions in the future, such as including use of multiple delivery methods, consideration of mobile device software, nutritionist educator, and curriculum-based approaches.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".