An exploration of how meal preparation activities relate to self-rated time pressure, stress, and health in Canada: A time use approach
Bibliographic record
Abstract
While previous work has provided a foundation for understanding the importance of the links between time use and diet, there has been little done to link time use to health outcomes. In this study, time use and self-rated health variables from the 2015 Time Use Cycle of Statistics Canada's General Social Survey are used to explore whether there are direct associations between time spent on meal preparation and health for Canadian Adults. In addition, this paper uses respondents' sequences of activities data from a time use diary to provide novel findings about the context of activities that precede and follow meal preparation. Proportional odds and logistic regression models are computed and show that there are significant relationships between spending more time on meal preparation and improved mental health and lower levels of stress. More time on meal preparation is also linked to general feelings of having less time. The analysis of activities preceding and following meal preparation activities demonstrates that individuals with different levels of self-rated stress or feelings of having extra time have significantly different activity sequence distributions (e.g., those reporting higher levels of stress are more likely to participate in chores and care activities). Exploring activity sequences related to meal preparation provides a first step in furthering the research community's grasp of the causal relationship between food-related time use and health and well-being outcome variables. Ultimately, this paper builds on the past literature on time use and meal preparation by establishing direct links between time spent on meal preparation activities, self-rated health and time use variables, in addition to offering insights into what activities surround this important activity via a novel sequence analysis.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".