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 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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".