Systematic review of technology‐mediated peer support interventions in paediatric type 1 diabetes care
Bibliographic record
Abstract
AIMS: There is increasing interest in the role of peer support in diabetes care. However, technology-mediated peer support in paediatric type 1 diabetes remains understudied.We aimed todescribe technology-mediated peer support interventions for children living with type 1 diabetes, their caregivers and healthcare providers. METHODS: CINAHL, Embase and MEDLINE (Ovid) were searched from Jan 2007 to June 2022. We included randomised and non-randomised trials with peer support interventions for children living with diabetes, their caregivers and/or healthcare providers. Studies examining clinical, behavioural or psychosocial outcomes were included. Quality was assessed with the Cochrane risk of bias tool. RESULTS: Twelve of 308 retrieved studies were included, with a study duration range of 3 weeks to 24 months and most were randomised trials (n = 8, 66.67%). Four technology-based interventions were identified: phone-based text messages, video, web portal and social media, or a hybrid peer support model. Most (58.6%, n = 7) studies exclusively targeted children with diabetes. No significant improvement was observed in psychosocial outcomes (quality of life, n = 4; stress and coping, n = 4; social support, n = 2). Mixed findings were observed in HbA1c (n = 7) and 28.5% studies (n = 2/7) reported reduced incidence of hypoglycaemia. CONCLUSIONS: Technology-mediated peer support interventions may have the potential to improve diabetes care and outcomes. However, further well-designed studies are necessary that address the needs of diverse populations and settings, and the sustainability of intervention effects.
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".