Peer support interventions in type 2 diabetes: Review of components and process outcomes
Bibliographic record
Abstract
BACKGROUND: This review seeks to identify (a) the various components and process outcomes of type 2 diabetes peer support (PS) interventions and (b) the measures implemented to monitor intervention fidelity and evaluate outcomes in these studies. METHODS: The MEDLINE, PubMed, EMBASE (Excerpta Medica Database), CENTRAL (Cochrane Central Register of Controlled Trials), CINAHL (Cumulative Index to Nursing and Allied Health Literature), and PsycINFO databases were searched from inception to May 2019. Two reviewers independently screened and extracted data from eligible articles via the Template for Intervention Description and Replication (TIDieR) checklist (why, what, who provided, how, where, when and how much, tailoring, modifications, and how well). RESULTS: Twenty-three trials were included. The total number of participants was 7178. Most interventions were in primary care. Although face-to-face was the most common modality of contact, rates of contact were highest for telephone. Potential peer leaders (PLs) were identified primarily through recommendations from health professionals, based on their communication skills, glycosylated hemoglobin (HbA1c), and coaching interest. PLs were mostly female, university educated, and had a long history of diabetes (≥ 10 years). PL training varied significantly in length and content; the two most frequent topics were communication skills and diabetes knowledge. Although several studies implemented methods to evaluate "intervention fidelity," only few rigorously assessed the two key components of fidelity, "adherence" and "competence," through audio- and video-taping or direct observations. CONCLUSIONS: The impact of PS on participants' health outcomes is well investigated; however, the implementation and evaluation strategies vary significantly across these studies. In the present review, we define the various components of PS interventions and propose suggestions for enhancing the implementation and evaluation of future PS models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".