Disentangling Time Use, Food Environment, and Food Behaviors Using Multi‐Channel Sequence Analysis
Bibliographic record
Abstract
Geographic access to food retailers has long been considered an important determinant of food‐related behaviors. Despite methodological improvements in assessing food environments, their associations with food behaviors have remained inconsistent. We argue that one possible reason for these inconsistencies is the lack of information about how an individual’s time use dynamics play out in space. To this point, few studies on the combined effects of food geography and time use on food behaviors exist, and methods to achieve such analyses have been underdeveloped. In this study, we propose a novel application of multi‐channel sequence analysis (MCSA) to identify joint patterns of time use and food‐related geographic contexts. We explore how those spatiotemporal patterns are associated with individuals’ food shopping and food‐related household chores. This analytical workflow is demonstrated using time use diaries and GPS trajectories collected in Toronto in 2019. This test case identifies spatiotemporal patterns with distinctive characteristics of disaggregated time use and spatial exposure to food retail and finds associations between these distinct space‐time patterns and participation in food‐related activities. This application of MCSA affords a promising novel approach for food environment researchers to perform nuanced assessments of the sequenced spatiotemporal contexts in which food‐related behaviors occur.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".