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Record W4398180147 · doi:10.1007/s12170-024-00737-6

Recent Findings on the Effectiveness of Peer Support for Patients with Type 2 Diabetes

2024· article· en· W4398180147 on OpenAlexaboutno aff
James J. Werner, Kelsey Ufholz, Prashant Yamajala

Bibliographic record

VenueCurrent Cardiovascular Risk Reports · 2024
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineType 2 diabetesDiabetes mellitusIntensive care medicineInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.544
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.263
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations21
Published2024
Admission routes1
Has abstractyes

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