Assessing the Validity of Commercial and Municipal Food Environment Datasets in Vancouver, Canada
Bibliographic record
Abstract
Objective: This study assessed systematic bias and the effects of dataset error on the validity of food environment measures in two municipal and two commercial secondary datasets. Design: Sensitivity, positive predictive value (PPV), and concordance were calculated by comparing two municipal and two commercial secondary datasets with ground-truthed data collected within 800m buffers surrounding 26 schools. Logistic regression examined associations between sensitivity and PPV with commercial density and neighborhood socioeconomic deprivation. Kendall's Tau estimated correlations between density and proximity of food outlets near schools constructed with secondary datasets versus ground-truthed data. Setting: Vancouver, Canada. Subjects: Food retailers located within 800m of 26 schools Results: All datasets scored relatively poorly across validity measures, though overall, municipal datasets had higher levels of validity than did commercial datasets. Food outlets were more likely to be missing from municipal health inspections lists and commercial datasets in neighborhoods with higher commercial density. Still, both proximity and density measures constructed from all secondary datasets were highly correlated (Kendall’s Tau> 0.70) with measures constructed from ground-truthed data. Conclusions: Despite relatively low levels of validity in all secondary datasets examined, food environment measures constructed from secondary datasets remained highly correlated with ground-truthed data. Findings suggest that secondary datasets can be used to measure the food environment, though estimates should be treated with caution in areas with high commercial density.
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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 | 0.013 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".