Activity space-based measures of the food environment and their relationships to food purchasing behaviours for young urban adults in Canada
Bibliographic record
Abstract
OBJECTIVE: To examine the potential links between activity spaces, the food retail environment and food shopping behaviours for the population of young, urban adults. DESIGN: Participants took part in the Canada Food Study, which collected information on demographics, food behaviour, diet and health, as well as an additional smartphone study that included a seven-day period of logging GPS (global positioning system) location and food purchases. Using a time-weighted, continuous representation of participant activity spaces generated from GPS trajectory data, the locations of food purchases and a geocoded food retail data set, negative binomial regression models were used to explore what types of food retailers participants were exposed to and where food purchases were made. SETTING: Toronto, Montreal, Vancouver, Edmonton and Halifax, Canada. SUBJECTS: Young adults aged 16-30 years (n 496). These participants were a subset of the larger Canada Food Study. RESULTS: Demographics, household food shopper status and city of residence were significantly associated with different levels of exposure to various types of food retailers. Food shopping behaviours were also statistically significantly associated with demographics, the activity space-based food environment, self-reported health and city of residence. CONCLUSIONS: The study confirms that food behaviours are related to activity space-based food environment measures, which provide a more comprehensive accounting of food retail exposure than home-based measures. In addition, exposure to food retail and food purchasing behaviours of an understudied population are described.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".