Exploring the prospective acceptability of a healthy food incentive program from the perspective of people with type 2 diabetes and experiences of household food insecurity in Alberta, Canada
Bibliographic record
Abstract
OBJECTIVE: FoodRx is a 12-month healthy food prescription incentive program for people with type 2 diabetes (T2DM) and experiences of household food insecurity. In this study, we aimed to explore potential users' prospective acceptability (acceptability prior to program use) of the design and delivery of the FoodRx incentive and identify factors influencing prospective acceptability. DESIGN: We used a qualitative descriptive approach and purposive sampling to recruit individuals who were interested or uninterested in using the FoodRx incentive. Semi-structured interviews were guided by the theoretical framework of acceptability, and corresponding interview transcripts were analysed using differential qualitative analysis guided by the socioecological model. SETTING: Individuals living in Alberta, Canada. PARTICIPANTS: In total, fifteen adults with T2DM and experiences of household food insecurity. RESULTS: 5). We identified four themes that captured factors that influenced users' prospective acceptability: (i) participants' confidence, views and beliefs of FoodRx design and delivery and its future use (intrapersonal), (ii) the shopping routines and roles of individuals in participants' social networks (interpersonal), (iii) access to and experience with food retail outlets (community), and (iv) income and food access support to cope with the cost of living (policy). CONCLUSION: Future healthy food prescription programs should consider how factors at all levels of the socioecological model influence program acceptability and use these data to inform program design and delivery.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".