Can peer educators influence healthy eating in people with diabetes? Results of a randomized controlled trial
Bibliographic record
Abstract
AIMS: To assess whether the Expert Patient Programme (EPP), adapted for people with Type 2 diabetes, can be used to promote healthy eating to improve glycaemic control. METHODS: Adults with Type 2 diabetes (n = 317) were randomized to receive either a diabetes-specific EPP (n = 162) or individual one-off appointments with a dietitian (control group) (n = 155). The diabetes-specific EPP followed the standard National Health Service programme although all participants in the group had diabetes only, rather than a mix of chronic conditions. Participants attended a group session for 2 h once per week for 6 weeks. In addition, a final seventh-week 2-h session was included that was specific to issues concerning diabetes. Outcomes were assessed at baseline, 6 and 12 months. RESULTS: There were no statistically significant differences between the control and the intervention group in any of the clinical outcomes measured. There was no significant difference between the groups in any dietary outcome. There was a higher starch intake in the EPP group, although this did not reach statistical significance (effect size for starch adjusted for baseline values 8.8 g; 95% CI -1.3 to 18.9). There was some loss of participants between baseline measurement and randomization, although this did not appear to have had an important impact on baseline balance. CONCLUSIONS: In this study of people with Type 2 diabetes, the EPP approach was not effective in changing measures of diabetes control or diet.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".