Experiences and perceived outcomes of a grocery gift card program for households at risk of food insecurity
Bibliographic record
Abstract
Purpose: Food support programs, such as I Can for Kids (IC4K) in Calgary, Alberta, Canada, aim to reduce the prevalence and severity of household food insecurity by providing grocery gift cards (GGC) to low-income households with children. There are currently no qualitative studies that have explored whether and how GGC programs influence food access among food insecure households. I explored program recipients’ and program deliverers’ experiences and perceived outcomes of receiving or distributing GGC from IC4K. Method: I used qualitative descriptive methodology for this study. Data generation and analysis were guided by Freedman et al’s theoretical framework of nutritious food access. Fifty-four participants were purposively recruited. Semi-structured interviews were conducted between August and November 2020 with 37 program recipients who accessed IC4K’s GGC program and 17 program deliverers who facilitated it. 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 deliverers’ experiences and perceived outcomes of receiving or distributing GGC, and suggestions to improve IC4K’s GGC program. Findings: Three themes were generated from the data. The first theme was related to how IC4K’s GGC program promoted a sense of autonomy and dignity among program recipients. The second theme was related to improved dietary patterns and food skills. The third theme was related to program logistical strengths and limitations, including the program’s impact on program deliverers’ connection with clients, their workload, experiences of differential access to GGC among recipients, and the importance of increasing program awareness to reach more food insecure households. Conclusion: IC4K’s GGC program enhanced recipients’ sense of autonomy and dignity and improved dietary patterns and food skills. Facilitating IC4K’s GGC program improved program deliverers’ connection with clients and reduced their overall workload. I also found experiences of differential access to GGC among recipients and the importance of increasing program awareness. I used my study findings to inform three recommendations to improve the experiences and perceived outcomes of future recipients who access IC4K’s GGC program: 1) increase the number of GGC; 2) establish concrete guidelines governing GGC distribution; and; 3) increase program awareness.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 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".