A Subsidized Healthy Food Prescription Program for Adults With Type 2 Diabetes Who Are Experiencing Food Insecurity: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
It is vital for individuals with type 2 diabetes (T2DM) to adhere to a healthy dietary pattern to maintain optimal blood glucose levels and overall health. Increasing costs of healthy foods, however, are a barrier to maintaining healthful dietary patterns, particularly for individuals with T2DM who are experiencing food insecurity. Poor diet quality may result in difficulties maintaining optimal blood glucose levels, leading to higher rates of diabetes complications, and increased acute care usage and costs. Although the adverse impacts of food insecurity on maintaining optimal blood glucose levels are well documented, effective strategies to this among individuals with T2DM are lacking. One approach is providing subsidies to purchase healthy foods through subsidized healthy food prescription programs. These programs may help reduce food insecurity and improve diet quality, thereby improving blood glucose levels and reducing diabetes complications over time. A parallel group randomized controlled trial will examine the effectiveness of a subsidized healthy food prescription program compared to a healthy food prescription alone in improving average blood glucose levels (primary outcome), and other secondary outcomes among 404 adults who are experiencing food insecurity and persistent hyperglycemia. The subsidized healthy food prescription program consists of two core elements: 1) A one-time healthy food prescription pamphlet that outlines an evidence-based healthy dietary pattern; 2) A healthy food subsidy of $1.50/day/household member to purchase healthy foods in participating supermarkets for 6 months. At baseline and 6-month follow-up, participants will provide responses to sociodemographic and health-related items, and a variety of patient-reported outcomes. Biochemical and physical measurements will also be obtained. The study's theory of change posits that reducing food insecurity and improving diet quality will be key mediators in improving blood glucose levels, which may reduce diabetes complications, and healthcare usage and costs over time. The results of this study will demonstrate if a subsidized healthy food prescription program results in meaningful changes in average blood glucose levels and other clinically relevant outcomes. Alberta Innovates, Alberta Health Services.
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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 | 0.027 | 0.029 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.008 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.094 | 0.014 |
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".