A Healthy Food Prescription Incentive Program for Adults With Type 2 Diabetes Who Are Experiencing Food Insecurity: Protocol for a Longitudinal Qualitative Study
Bibliographic record
Abstract
Food insecurity is inadequate or insecure access to food due to financial constraints. Individuals experiencing food insecurity tend to have poorer diet quality compared to their food secure peers. Given the importance of maintaining healthful dietary patterns for optimal glycemic control, food insecurity is a considerable barrier to managing type 2 diabetes (T2DM). Healthy food prescription incentive programs aim to reduce such barriers by providing financial incentives to purchase healthy foods. The purpose of this study is to conduct longitudinal qualitative shop-along interviews among adults with T2DM who are experiencing food insecurity. We will explore experiences of redeeming healthy food incentives and factors influencing food purchasing patterns during participation in a healthy food prescription program in Alberta, Canada. The healthy food prescription incentive program consists of two core elements: 1) A one-time healthy food prescription pamphlet outlining an evidence-based healthy dietary pattern; and 2) A healthy food incentive of $1.50/day/household member to purchase healthy foods in participating supermarkets for 6 months. Thirty participants aged 18–85 years will be purposefully recruited from primary care clinics. At baseline and 6-month follow-up, participants will take part in supermarket-based shop-along qualitative interviews. Participants will be asked to complete a usual grocery shop alongside a researcher. Using think-aloud principles, they will be asked to verbalize their thought processes for the shop's duration. Researchers will also collect naturalistic observations of participants, such as consulting nutrition labels. A post-shop interview will be completed to discuss experiences, decision-making rationale, and potential barriers and facilitators to food purchasing. Data will be analyzed iteratively and inductively at each time point. Further, a longitudinal analysis will compare emerging themes and identify changes occurring over time. N/A This study will generate key data regarding if, how, and why such programs may address barriers to maintaining healthful dietary patterns. These findings will help to understand experiences of participating in healthy food incentive programs that can be used to improve future programs. Alberta Innovates.
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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 | 0.043 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.040 | 0.005 |
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".