A Subsidized Healthy Food Prescription Program for Adults with type 2 Diabetes who are Experiencing Food Insecurity: Protocol for a Randomized Controlled Trial, Modelling and Implementation Studies
Bibliographic record
Abstract
Abstract Background: The high cost of many healthy foods poses a significant challenge to the maintenance of optimal blood glucose levels for adults with type 2 diabetes (T2DM) who are experiencing food insecurity, leading to diabetes complications and excess acute care usage and costs. Subsidized healthy food prescription programs may help to reduce food insecurity by financially supporting patients to improve their diet quality, prevent diabetes complications and avoid acute care use. This study will use a type 2 hybrid effectiveness-implementation design to examine the reach, effectiveness, adoption, implementation and maintenance (RE-AIM) of a subsidized healthy food prescription program for adults who are experiencing food insecurity and persistent hyperglycemia. A randomized controlled trial (RCT) will investigate program effectiveness via impact on blood glucose levels (primary outcome), food insecurity, diet quality and other clinical and patient-reported outcomes. A modelling study will estimate longer-term program effectiveness in reducing diabetes-related complications, resource use and costs. An implementation study will examine all RE-AIM domains, including reasons behind program successes and failures, fidelity, mechanisms of impact, contextual determinants of effective implementation and sustainability. Methods: 404 adults who are experiencing food insecurity and persistent hyperglycemia, including adults who identify as Indigenous, will be randomized to a subsidized healthy food prescription intervention (n=202) or a healthy food prescription comparison group (n=202). Both groups will receive a healthy food prescription. The intervention group will additionally receive $1.50/day/household member to purchase healthy foods in supermarkets for 6 months. The implementation process will follow the Quality Implementation Framework. Outcomes will be assessed at baseline and follow-up (6 months) in the RCT and analyzed using mixed-effects linear and multinomial logistic/ordinal regression models. Longer-term outcomes will be modelled using the validated UK Prospective Diabetes Study outcomes simulation model-2. Implementation processes and outcomes will be continuously measured via quantitative and qualitative data and analyzed using descriptive statistics and theory-informed directed content analysis, respectively. Discussion: This research will provide a comprehensive body of data with high internal and external validity to assist policymakers and practitioners to effectively and rapidly translate the evidence generated into programs and policies to support patients with T2DM who are experiencing food insecurity. Trial registration: ClinicalTrials.gov NCT04725630 (January 25, 2021; https://www.clinicaltrials.gov/ct2/show/NCT04725630?term=Subsidized+Healthy+Food+Prescription+Program&cond=Diabetes+Mellitus%2C+Type+2&draw=2&rank=1).
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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 | 0.057 | 0.051 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.008 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 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) | 0.074 | 0.013 |
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".