I Can for Kids: Experiences and Perceived Outcomes of a Summer Food Program for Low-Income Households at Risk of Food Insecurity in Calgary, Alberta, Canada
Bibliographic record
Abstract
The prevalence of household food insecurity increases in the summer when school meals are no longer accessible, which can negatively impact children's health and wellbeing. Summer food programs, such as I Can for Kids (IC4K) in Calgary, Alberta, Canada, aim to reduce food insecurity in low income households with school-aged children during the summer months. Qualitative studies have not yet examined whether or how grocery gift cards (GGC) can reduce experiences of food insecurity among low-income households. We explored recipients’ and agency staff experiences and perceived outcomes of receiving or distributing GGC from IC4K. 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 38 primary caregivers (i.e., GGC recipients) and 17 agency staff who distributed GGC. Directed content analysis was used to analyze the data from each set of interviews using a deductive-inductive approach. Codes were combined into themes that summarized GGC recipients’ and agency staff's experiences and perceived outcomes of receiving or distributing GGC, and feedback to improve program delivery. Three themes were generated from the data. The first theme was Financial Relief and reflected increased self-efficacy, improved diet quality, and reduced household stress from receiving GGC. The second theme was Social Connectedness and described enriched family relationships, enhanced rapport between staff and recipients, and increased recipient social capital. The last theme was Program Enhancements and described feedback to improve program delivery by extending program duration, increasing strategic direction to staff on GGC distribution, and additional promotional efforts to increase awareness of GGC availability. GGC recipients and agency staff perceived that GGC offered financial relief and enhanced social connections for recipients, and suggested areas for program improvements. Study findings can inform improvements to summer food programs that deliver GGC to reduce food insecurity among low-income households in the summer. Funding provided by the O'Brien Institute for Public Health at the University of Calgary.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 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".