A method for neighborhood‐level surveillance of food purchasing
Bibliographic record
Abstract
Added sugar, particularly in carbonated soft drinks (CSDs), represents a considerable proportion of caloric intake in North America. Interventions to decrease the intake of added sugar have been proposed, but monitoring their effectiveness can be difficult due to the costs and limitations of dietary surveys. We developed, assessed the accuracy of, and took an initial step toward validating an indicator of neighborhood-level purchases of CSDs using automatically captured store scanner data in Montreal, Canada, between 2008 and 2010 and census data describing neighborhood socioeconomic characteristics. Our indicator predicted total monthly neighborhood sales based on historical sales and promotions and characteristics of the stores and neighborhoods. The prediction error for monthly sales in sampled stores was low (2.2%), and we demonstrated a negative association between predicted total sales and median personal income. For each $10,000 decrease in median personal income, we observed a fivefold increase in predicted monthly sales of CSDs. This indicator can be used by public health agencies to implement automated systems for neighborhood-level monitoring of an important upstream determinant of health. Future refinement of this indicator is possible to account for factors such as store catchment areas and to incorporate nutritional information about products.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".