Working With Community Partners to Implement and Evaluate the Chicago Park District’s 100% Healthier Snack Vending Initiative
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The objective of this case study was to evaluate the acceptability, sales impact, and implementation barriers for the Chicago Park District's 100% Healthier Snack Vending Initiative to strengthen and support future healthful vending efforts. COMMUNITY CONTEXT: The Chicago Park District is the largest municipal park system in the United States, serving almost 200,000 children annually through after-school and summer programs. Chicago is one of the first US cities to improve park food environments through more healthful snack vending. METHODS: A community-based participatory evaluation engaged community and academic partners, who shared in all aspects of the research. From spring 2011 to fall 2012, we collected data through observation, surveys, and interviews on staff and patron acceptance of snack vending items, purchasing behaviors, and machine operations at a sample of 10 Chicago parks. A new snack vending contract included nutrition standards for serving sizes, calories, sugar, fat, and sodium for all items. Fifteen months of snack vending sales data were collected from all 98 snack vending machines in park field houses. OUTCOMES: Staff (100%) and patrons (88%) reacted positively to the initiative. Average monthly per-machine sales increased during 15 months ($84 to $371). Vendor compliance issues included stocking noncompliant items and delayed restocking. INTERPRETATION: The initiative resulted in improved park food environments. Diverse partner engagement, participatory evaluation, and early attention to compliance can be important supports for healthful vending initiatives. Consumer acceptance and increasing revenues can help to counter fears of revenue loss that can pose barriers to adoption.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it