How does fresh food prescribing fit into the social service landscape? A qualitative study in Ontario, Canada
Bibliographic record
Abstract
INTRODUCTION: Food prescription programs are part of the broader social prescribing movement as an approach to address food insecurity and suboptimal diet in health care settings. These programs exist amid other social services, including income-based supports and food assistance programs; however, evaluations of the interactions between these programs and pre-existing services and supports are limited. This study was embedded within a larger evaluation of the 52-week Fresh Food Prescription (FFRx) program (April 2021-October 2022); the objective of this study was to examine how program participation influenced individuals' interactions with existing income-based supports and food assistance programs. METHODS: This study was conducted in Guelph, Ontario, Canada. One-to-one (n = 23) and follow-up (n = 10) interviews were conducted to explore participants' experiences with the program. Qualitative data were analyzed thematically using a constant comparative analysis. RESULTS: Participants described their experience with FFRx in relation to existing income-based supports and food assistance programs. FFRx reportedly extended income support further to cover living expenses, allowed participants to divert income to other necessities, and reduced the sacrifices required to meet basic needs. FFRx lessened the frequency of accessing other food assistance programs. Aspects of FFRx's design (e.g. food delivery) shaped participant preferences in favour of FFRx over other food supports. CONCLUSION: As food prescribing and other social prescribing programs continue to expand, there is a need to evaluate how these initiatives interact with pre-existing services and supports and shape the broader social service landscape.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.032 | 0.012 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".