Experiences and Perceived Outcomes of a Grocery Gift Card Program for Low-Income Households at Risk of Food Insecurity
Bibliographic record
Abstract
Household food insecurity negatively affects children's diet quality, physical and mental health, and academic performance. I Can for Kids’ (IC4K) grocery gift card (GGC) program provides GGC to low-income households with school-aged children at risk of household food insecurity in Calgary, Canada. This study described program recipients’ and facilitators’ experiences and perceived outcomes of accessing or facilitating IC4K's GGC program. This study used qualitative descriptive methodology. Data generation and analysis were guided by Freedman et al's theoretical framework of nutritious food access. Semi-structured interviews were conducted between August and November 2020 with 37 program recipients and 17 program facilitators who were purposively recruited. Directed content analysis was used to analyze the data using a deductive-inductive approach. Codes were combined into subthemes and themes that summarized program recipients’ and facilitators’ experiences and perceived outcomes of receiving or distributing GGC, and suggestions to improve IC4K's GGC program. Three themes were generated from the data: 1) IC4K's GGC program promoted a sense of autonomy and dignity among program recipients; 2) recipients used GGC to purchase foods that improved household dietary patterns and food skills, including foods that aligned with health-related food needs and cultural foodways and; 3) program logistical strengths and limitations, including the program's impact on facilitators’ connection with clients and their workload, experiences of differential access to GGC among recipients, and the importance of increasing program awareness to reach more food insecure households. IC4K's GGC program enhanced recipients’ access to nutritious foods, had positive impacts on household finances and diet quality, as well as recipients’ social health and emotional wellbeing. However, differential access to GGC among recipients was also identified. Study findings were used to inform three recommendations to improve IC4K's GGC program: 1) increase the number of GGC that recipients can receive; 2) establish concrete guidelines governing GGC distribution and; 3) increase program awareness. O'Brien Institute for Public Health.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".