Assessing the Effect of a Food Voucher on the Dietary Intake of Patients with Diabetes Using the Canadian Diet History Questionnaire III: A Randomized Control Trial
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: The high cost of healthy foods makes it difficult for people with a low income to manage diabetes. This study examined the effects of a monthly grocery voucher on the dietary intake, assessed through the Canadian Diet History Questionnaire III, of diabetes patients facing food or financial insecurity. We also assessed the impact on levels of hemoglobin A1c, beta-carotene, and ascorbic acid. METHODS: Participants were randomly selected from a larger clinical trial and completed the survey at 6-month follow-up. RESULTS: Voucher recipients consumed more whole fruit (mean difference in daily servings, MD 0.8; 95% CI [0.1, 1.6]) and fewer refined grains (MD -1.0; 95% CI [-1.9, -0.1]). For other food groups, the confidence intervals for the difference included null effect. Mean HEFI-2019 score was 51.7 out of 80, with voucher recipients averaging 52.4 vs. 51.0 for controls (MD 1.4; 95% CI [-3.6, 6.1]). The voucher group showed a slight HbA1c decrease (MD -0.4; 95% CI [-1.4, 0.5]). CONCLUSIONS: A voucher providing access to healthy foods for people with diabetes or prediabetes slightly increased intake of fruits and decreased intake of refined grains. Larger interventional studies are needed to determine the effects of vouchers on dietary intake among this population.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".