The Impact of Providing Material Benefits to Improve Access to Food on Clinical Parameters, Dietary Intake, and Household Food Insecurity in People with Diabetes: A Systematic Review with Narrative Synthesis
Bibliographic record
Abstract
The high cost of healthy foods makes maintaining a healthy dietary pattern challenging, particularly among people with diabetes who are experiencing food insecurity. The objectives of this study were to: 1) review evidence on the impact of providing material benefits (e.g., food coupons/vouchers, free food, or financial subsidies/incentives) to improve access to food on clinical parameters, dietary intake, and household food insecurity in people with diabetes, and 2) review relevant economic evidence. Six databases were searched from inception to March 2023 for longitudinal studies with quantitative outcomes. Twenty-one studies were included in the primary review and 2 in the economic analysis. Risk of bias was high in 20 studies and moderate in 1 study. The number of randomized controlled trials and nonrandomized studies reporting statistically significant improvement, alongside Grading of Recommendations Assessment, Development, and Evaluation (GRADE) certainty of the evidence was: HbA1c: 1/6 and 4/12 (very low), systolic blood pressure: 0/3 and 1/8 (very low), diastolic blood pressure: 0/3 and 1/7 (very low), BMI: 0/5 and 2/8 (very low), body weight: 0/0 and 1/3 (very low), hypoglycemia: 1/2 and 1/2 (very low), daily intake of fruits and vegetables: 1/1 and 1/3 (very low), daily intake of whole grains: 0/0 and 0/2 (very low), overall diet quality: 2/2 and 1/1 (low), and household food insecurity: 2/3 and 0/0 (very low). The 2 studies included in the economic analysis showed no difference in Medicare spending from Supplemental Nutrition Assistance Program participation and cost-savings from medically tailored meals in an economic simulation. Overall, providing material benefits to improve access to food for people with diabetes may improve household food insecurity, fruit and vegetable intake, and overall diet quality, but effects on clinical parameters and whole grain intake are unclear. The certainty of evidence was very low to low by GRADE. PROSPERO (CRD42021212951).
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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 | 0.013 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".