Effectiveness of a Sliding Scale Payment Model at a Community Food Market to Reduce Customer Food Insecurity Status
Bibliographic record
Abstract
Food insecurity is a global public health challenge, with those affected having inadequate or insecure access to food due to financial constraints. This study determined the effectiveness of reducing community food insecurity by implementing a sliding scale payment model approach at a local community food market in Guelph ON, Canada. In this payment model, fresh produce could be purchased at the market within a price range along a sliding scale, wherein lower income customers can confidentially select to pay prices at the lower end of the payment scale, whereas those with higher household incomes can select to pay the higher payment option. In this pilot study, customers of the community food market (n=119) were surveyed to determine their food insecurity status both prior to and after regularly shopping at the food markets, and how using the sliding scale payment model impacted their access to affordable produce. Market attendance was shown to reduce customers self-reported indicators of food insecurity (P<0.05). Additionally, customer household income levels were correlated with the price they paid along the sliding scale; wherein lower and higher income households paid for produce at a corresponding level on the payment scale. These results demonstrate that the sliding scale payment model is supported by the community across household income levels and was successful at reducing customer food insecurity. This model could be implemented in other communities to reduce food insecurity.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".