Exploring sociodemographic and nutrition-related correlates of meal-kit use across five countries: findings from the International Food Policy Study
Bibliographic record
Abstract
Abstract Objective: To assess the frequency and correlates of meal-kit use across five countries using population-level data. Design: Online surveys conducted in 2022 assessed meal-kit use in the past week. Binary logistic regression models examined sociodemographic and nutrition-related correlates of meal-kit use, including self-reported home meal preparation and cooking skills, commercially prepared meal consumption and healthy eating, weight change and sustainability efforts. Setting: Canada, Australia, the UK, the USA and Mexico. Participants: 20,401 adults aged 18–100 years. Results: Overall, 14 % of participants reported using meal-kits in the past week. Use was highest in the USA (18 %) and lowest in Canada (9 %). Meal-kit use was greater among individuals who were younger, male, of minority ethnicity, had high educational attainment, had higher income adequacy or had children living in the household ( P < 0·01 for all). Use was greater for those who participated in any food shopping ( v . none), those who prepared food sometimes (3–4 d/week or less v . never) and those who reported ‘fair’ or better cooking skills ( v . poor; P < 0·05 for all). Consuming any ‘ready-to-eat’ food ( v . none) and visiting restaurants more recently ( v . > 6 months ago; P < 0·001 for all) were associated with greater meal-kit use. Eating fruits/vegetables more than 2 times/d and engaging in diet modification efforts were also associated with increased meal-kit use, as was engaging in weight change or sustainability efforts ( P < 0·001 for all). Conclusions: Meal-kits tend to be used by individuals who make efforts to support their health and sustainability, potentially valuing ‘convenient’ alternatives to traditional home meal preparation; however, use is concentrated amongst those with higher income adequacy.
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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 | 0.003 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".