How is grocery shopping completed in households with children? Gender gaps and typologies of grocery shopping in four Canadian metropolises
Bibliographic record
Abstract
Grocery shopping is important household labor that directly impacts diet quality and related downstream health outcomes. Like other household tasks, it is usually divided unequally in opposite-gender households, with women doing more grocery shopping than men. However, common indicators used to identify gender gaps, like activity frequency and duration, are unable to sufficiently depict the full picture of the constraints women may face when engaging in grocery shopping activities. This is especially evident for women in households with children, who often share more care-related labor. To address this gap, this paper examines the gender differences in grocery shopping activities in multiple dimensions, including frequency, duration, grocery store types, travel modes, the presence of companions, time of day, and trip chaining. Drawing upon the Time Use & Food Habits survey conducted in four Canadian cities in 2021, the results show that women and men in households with children exhibit different characteristics of grocery shopping across multiple dimensions. Women compared to men not only spend longer time shopping, but also have a smaller proportion of driving to grocery stores and a larger proportion of shopping during working hours and with companions. Gender differences were further compared among different classifications of grocery shopping patterns identified through latent class analysis. Various gender gaps are found across different classifications, with women shopping with others possibly having some of the most complex constraints. Multinominal logistic regression shows that the shopping with others is associated with relatively lower socioeconomic status, more care responsibilities, and living in an urban area. Overall, this study provides evidence of nuanced gender gaps of grocery shopping in multiple dimensions, within different groups of people, and across a range of cities of various sizes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".