Analyzing differences between spatial exposure estimation methods: A case study of outdoor food and beverage advertising in London, Canada
Bibliographic record
Abstract
Exposure assessment in the context of mobility-oriented health research often is challenged by the type of spatial measurement technique used to estimate exposures to environmental features. The purpose of this study is to compare smartphone global positioning system (GPS), shortest network path mobility, and buffer-based approaches in estimating exposure to outdoor food and beverage advertising among a sample of 154 teenagers involved in the SmartAPPetite study during 2018 in London, Ontario, Canada. Participants were asked to report their home postal code, age, gender identity, ethnicity, and number of purchases they had made at a retail food outlet in the past month. During the same time period, a mobile phone application was used to log their mobility and specifically record when a participant was in close proximity to outdoor advertising. The results of negative binomial regression modelling reveal significant differences in estimates of advertising exposure, and the relationship to self-reported purchasing. Spatial exposure estimation methods showed differences across regression models, with the buffer and observed GPS approaches delivering the best fitting models, depending on the type of retail food outlet. There is a clear need for more robust research of spatial exposure measurement techniques in the context of mobility and food (information) environment research.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".