Experiences and perceived outcomes of a grocery gift card programme for households at risk of food insecurity
Bibliographic record
Abstract
OBJECTIVE: This study explored programme recipients' and deliverers' experiences and perceived outcomes of accessing or facilitating a grocery gift card (GGC) programme from I Can for Kids (iCAN), a community-based programme that provides GGC to low-income families with children. DESIGN: This qualitative descriptive study used Freedman et al's framework of nutritious food access to guide data generation and analysis. Semi-structured interviews were conducted between August and November 2020. Data were analysed using directed content analysis with a deductive-inductive approach. PARTICIPANTS: Fifty-four participants were purposively recruited, including thirty-seven programme recipients who accessed iCAN's GGC programme and seventeen programme deliverers who facilitated it. SETTING: Calgary, Alberta, Canada. RESULTS: Three themes were generated from the data. First, iCAN's GGC programme promoted a sense of autonomy and dignity among programme recipients as they appreciated receiving financial support, the flexibility and convenience of using GGC, and the freedom to select foods they desired. Recipients perceived these benefits improved their social and emotional well-being. Second, recipients reported that the use of GGC improved their households' dietary patterns and food skills. Third, both participant groups identified programmatic strengths and limitations. CONCLUSION: Programme recipients reported that iCAN's GGC programme provided them with dignified access to nutritious food and improved their households' finances, dietary patterns, and social and emotional well-being. Increasing the number of GGC provided to households on each occasion, establishing clear and consistent criteria for distributing GGC to recipients, and increasing potential donors' awareness of iCAN's GGC programme may augment the amount of support iCAN could provide to households.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".