Pilot environmental intervention trial in carry‐outs in low‐income neighborhoods of Baltimore City
Bibliographic record
Abstract
Among low‐income African Americans in Baltimore City, a significant proportion of daily caloric intake is derived from prepared foods served at carry‐outs. However, little is known about the city's carry‐out food environment and strategies to improve accessibility and consumption of healthy foods in these settings. We conducted a two‐month pilot intervention trial in 3 carry‐outs in low income areas of the city. We assessed acceptability of environmental interventions including: replacing the menu board to promote existing healthy menu options with photos, promotional posters, and the substitution of low‐fat for regular mayonnaise on sandwiches. All 3 carry‐out owners were Korean Americans who owned their restaurants for 15–29 years. Each carry‐out conducted business from behind Plexiglas, with little direct customer contact. Acceptability and fidelity of the new menu board and poster interventions were high (3 out of 3) and use of low‐fat mayo was moderate (2 out of 3). Perceived sustainability of the modified menu board and poster interventions were high (3 out of 3), but low‐fat mayo was low (0 out of 3). Low fat mayo purchasing was the primary barrier, given the lack of bulk availability and price at wholesalers. Our findings will be used to test the feasibility and impact of these carryout interventions using a larger sample size. Grant Funding Source : Diabetes Research and Traning Center
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".