Improving the Food Environment in Baltimore City through Policy Change
Bibliographic record
Abstract
The food environment, including availability and price, can greatly influence chronic disease risk. Policy interventions have the potential to institutionalize and sustain improvements in the food environment. This study was initiated in response to a request from Baltimore City (BC) lawmakers to develop feasible criteria for improving small food stores. Our mixed methods design included a literature review of small store interventions (N=18), in‐depth interviews with program staff (N=11), and a structured survey with an expert panel of stakeholders (N=54). Using our initial findings, we created a set of 15 criteria, which were ranked by the panel for anticipated effectiveness. The highest ranked criteria included: 1) stocking fresh fruit; 2) using shelf labels; 3) displaying point of purchase posters; 4) adhering to food stamp and WIC guidelines; 5) stocking fresh vegetables. We then developed 10 key policy recommendations including: 1) the designation of “high risk zones” (<.25 miles from a school; >.25 miles from a supermarket and >40% of population below poverty line); 2) mandated adherence to criteria 1–5; 3) regular Health Department inspections; 4) license probation for noncompliance; 5) a graded incentivization program; 6) a stakeholder advisory board. These recommendations and the associated criteria have the potential to directly impact BC legislation, and improve the health of its residents. Grant Funding Source : Johns Hopkins Center for a Livable Future
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".