Assessing the implementation and outcomes of a food prescription program in Ontario, Canada: A realist evaluation
Bibliographic record
Abstract
Background: Social prescribing has grown in popularity around the world as a method for health care practitioners to address the social determinants of health. Social prescribing is the process of a practitioner identifying a non-medical, social need in a patient, and then developing a non-medical prescription to connect them to community services. A subset of social prescribing is food prescribing, in which patients who are typically identified as food insecure are connected with services to provide access to nutritious foods. The Fresh Food Prescription Program (FFRx) was implemented beginning in 2021 by the SEED, a working group of the Guelph Community Health Centre (GCHC). Clients of the GCHC who were identified as food insecure and experiencing a cardiometabolic health condition were provided weekly vouchers for fruits and vegetables at the SEED’s online grocery store. \n \nResearch question: The objectives of this research were 1) to describe the experiences of participants with FFRx 2) to evaluate impacts of FFRx on household food security, diet patterns, health, and well-being and 3) to identify how various contexts and mechanisms shaped differential program experiences and outcomes among FFRx participants. \n \nMethods: Semi-structured interviews (n=23) were conducted with FFRx participants along with follow-up focus groups and individual discussions (n=10). Guided by realist evaluation, a hybrid thematic analysis was utilized to identify context, mechanisms, and outcomes in the data. \n \nResults: Three key program outcomes were identified: 1) increased food access; 2) improved physical health and diet quality; and 3) improved mental health. Participants shared that they enjoyed having more food available to them and were able to purchase produce that was previously inaccessible due to financial constraints. Participants also noted that they consumed more fruits and vegetables during the program, as well as less nutrient poor foods. As a consequence, many participants associated their increased consumption of fruits and vegetables with improved physical health symptoms, more energy, and better sleep. Participants highlighted that they felt less stress throughout the program due to the stability of food access, increased social connections, and improved self-esteem. \n \nDiscussion and conclusion: This study builds on current understandings of food prescribing, through demonstrating how these program can benefit participants through enhancing food access as well as self-reported physical and mental health. Importantly, this study also acknowledges the need for long-term, sustainable programming and funding to support food prescribing initiatives. The research elucidated the importance of developing programs that are context-aware and include supportive mechanisms that foster agency among participants. Further, this research serves as a starting point for future realist evaluations to be conducted, and highlights program design elements that could be implemented in future food prescribing programs.
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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 | 0.025 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".