An Evaluation Case Study on a Food Prescription Program in the Saskatchewan Prairies
Bibliographic record
Abstract
Food insecurity (FI) is a rising problem in Canada and has worsened since the COVID-19 pandemic. FI manifests differently in various populations but can be described as the lack of access to nutritious food due to various constraints, such as income, education, health status, land location, and ethnicity. Food prescription programs (FPP) are an increasingly popular way to help mitigate the effects of lack of access to food and affordability. This is an evaluation case study to understand the experiences of those participating in an FPP in Saskatchewan. Recruiting participants from Regina and North Battleford, SK, ten semi-structured interviews with thematic analysis explored the barriers to program participation, perceived effects on participants’ overall health, and the extent to which the program addressed FI. Findings from this study revealed five key themes: 1) Understanding the Challenges of Accessing Nutritious Food, 2) Acculturing to the New Access to Nutritious Food, 3) I Feel Empowered, 4) Health Impacts of the Food Prescription Program, and 5) We Want More Input and Support. Participants reported feeling better physically, enjoying social interactions with volunteers, and being more involved with family and friends through cooking. Suggestions for program improvement encompassed the desire for more nutritional and culinary education, more choice in what goes into the weekly food bag, expanding the program beyond the hospital setting, and changing the pick-up design in North Battleford. Social prescribing is new for many nurses. To help form a well-rounded program for a food-specific program, an RN would need to involve other team members, such as a dietitian, pharmacist, and social worker. Nurses who work in the acute care inpatient setting would benefit from learning more about social prescribing, as it is established that the social determinants of health, such as income, education, and food insecurity, greatly influence health status.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".