A rapid systematic review of the effect of health or peer volunteers for diabetes self-management: Synthesizing evidence to guide social prescribing
Bibliographic record
Abstract
Social prescribing is a model of care, usually in the community-setting, which aims to address people's unmet social needs. Volunteers support primary health care and community-based care in non-medical roles. However, few studies focus on volunteers in social prescribing, therefore, aimed to synthesize the effect of health or peer volunteer-led interventions on psychosocial and behavioural outcomes for middle-aged and older adults with Type 2 Diabetes Mellitus (T2DM) to inform future work for volunteering in social prescribing. We followed Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines and searched six databases and Google Scholar for peer-reviewed studies from 2013+ (last search May 16, 2024). We included randomized controlled trials (RCTs) from all languages, and synthesized data using the Cochrane's Synthesis Without Meta-analysis (SWiM) guidelines; and assessed risk of bias using the "Risk of Bias 2 Tool". We identified nine RCTs (reported in 10 publications). Interventions aimed to promote self-management of T2DM, and study duration ranged from one to 46 months. Training for volunteers varied between one to 32 hours, and most volunteers were offered a stipend. For psychosocial outcomes, only one outcome on social support favoured the intervention group, with the remaining outcomes reporting no differences between study groups. For behaviour, six outcomes (from three studies) favoured the intervention group, and for three outcomes there were no differences between study groups. In conclusion, volunteers bring a unique perspective to health interventions, but volunteer training, matching and retention, as well as intervention mode and duration, and geographical context need to be thoughtfully considered as important implementation factors. This work generates ideas for future studies focused on volunteers and T2DM management and social prescribing. Trial registration: PROSPERO registration: CRD42023453506.
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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 | 0.102 | 0.306 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.025 | 0.023 |
| Bibliometrics | 0.030 | 0.021 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".