Corner Store Retailers’ Perspectives on a Discontinued Healthy Corner Store Initiative
Bibliographic record
Abstract
Making fresh fruits and vegetables (FFV) more widely available has been a prominent focus of healthy retail interventions and may have an important role in improving food access and diet quality at the population level. 'Healthy retail' interventions in corner/convenience stores (CS) are increasingly being adopted by public health practitioners to address the diet-related risk factors, improve food access at the community level, and change food retail environments. Private sector retailers are integral to the success of public health retailing interventions, making their perspectives and experiences critical. There is a particular need for greater evidence from retailers in settings where evaluations of these interventions have yielded null or mixed results. Through semi-structured interviews with 8 CS retailers (7 from urban settings and 1 from rural) in Ottawa, Ontario, Canada, this study aimed to describe experiences and critical factors regarding the feasibility and sustainability of a healthy CS program that was not sustained following the pilot testing phase, with a specific focus on the sale of FFV. Thematic analysis was used to analyze the interview data, which indicated that retailers faced two dominant challenges with selling FFV in CS: both relate to how these stores are embedded in the larger local and global food system. We join others in arguing that efforts and support for retail interventions aiming to increase the availability of FFV in CS need to address the structure and relations of the food system, as an upstream determinant of CS retailer interest and motivation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".