Recent Findings on the Effectiveness of Peer Support for Patients with Type 2 Diabetes
Bibliographic record
Abstract
Abstract Purpose of Review To review randomized controlled trials (RCTs) published from 2021–2023 that reported the effects of peer support interventions on outcomes in patients with type 2 diabetes (T2DM). Recent Findings Literature searches yielded 137 articles and nine RCTs were ultimately reviewed. The reviewed trials involved in-person support groups, peer coach/mentor support, cultural peer support by community health workers, peer support during shared medical appointments (SMAs) including virtual reality-based SMAs, telehealth-facilitated programs, and telephone peer support. Most interventions combined two or more peer support strategies. Peer support was associated with significant decreases in HbA1c in 6 of the 9 reviewed studies. The largest statistically significant improvements in HbA1c were reported in a study of community health workers in Asia (-2.7% at 12 months) and a Canadian study in which trained volunteer peer coaches with T2DM met with participants once and subsequently made weekly or biweekly phone calls to them (-1.35% at 12 months). Systolic blood pressure was significantly improved in 3 of 9 studies. Summary The findings suggest that peer support can be beneficial to glycemic control and blood pressure in T2DM patients. Studies of peer support embedded within SMAs resulted in significant reductions in HbA1c and suggest that linkages between healthcare systems, providers, and peer support programs may enhance T2DM outcomes.
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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 | 0.015 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".