Healthy food prescription incentive programme for adults with type 2 diabetes who are experiencing food insecurity: protocol for a randomised controlled trial, modelling and implementation studies
Bibliographic record
Abstract
INTRODUCTION: The high cost of many healthy foods poses a challenge to maintaining optimal blood glucose levels for adults with type 2 diabetes mellitus who are experiencing food insecurity, leading to diabetes complications and excess acute care usage and costs. Healthy food prescription programmes may reduce food insecurity and support patients to improve their diet quality, prevent diabetes complications and avoid acute care use. We will use a type 2 hybrid-effectiveness design to examine the reach, effectiveness, adoption, implementation and maintenance (RE-AIM) of a healthy food prescription incentive programme for adults experiencing food insecurity and persistent hyperglycaemia. A randomised controlled trial (RCT) will investigate programme effectiveness via impact on glycosylated haemoglobin (primary outcome), food insecurity, diet quality and other clinical and patient-reported outcomes. A modelling study will estimate longer-term programme effectiveness in reducing diabetes-related complications, resource use and costs. An implementation study will examine all RE-AIM domains to understand determinants of effective implementation and reasons behind programme successes and failures. METHODS AND ANALYSIS: 594 adults who are experiencing food insecurity and persistent hyperglycaemia will be randomised to a healthy food prescription incentive (n=297) or a healthy food prescription comparison group (n=297). Both groups will receive a healthy food prescription. The incentive group will additionally receive a weekly incentive (CDN$10.50/household member) to purchase healthy foods in supermarkets for 6 months. Outcomes will be assessed at baseline and follow-up (6 months) in the RCT and analysed using mixed-effects regression. Longer-term outcomes will be modelled using the UK Prospective Diabetes Study outcomes simulation model-2. Implementation processes and outcomes will be continuously measured via quantitative and qualitative data. ETHICS AND DISSEMINATION: Ethical approval was obtained from the University of Calgary and the University of Alberta. Findings will be disseminated through reports, lay summaries, policy briefs, academic publications and conference presentations. TRIAL REGISTRATION NUMBER: NCT04725630. PROTOCOL VERSION: Version 1.1; February 2022.
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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 | 0.052 | 0.049 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.009 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.089 | 0.016 |
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".