Prevalence of online food delivery platforms, meal kit delivery, and online grocery use in five countries: an analysis of survey data from the 2022 International Food Policy Study
Bibliographic record
Abstract
BACKGROUND: Online food retail use is rapidly increasing in popularity, and offers user-friendly apps, and new food delivery models, including online food delivery platforms, online grocery retailers, and online meal kit delivery. We aimed to: (1) quantify the prevalence of online food retail platform use by adults across Australia, Canada, Mexico, the United Kingdom and the United States, and to (2) assess the associations between sociodemographic and behavioural factors and use of online food retail platforms. METHODS: A cross-sectional online survey was conducted with adults as part of the 2022 International Food Policy Survey (n = 19,877). We described the frequency of use and number of meals ordered using different online food retail and delivery platforms. Logistic regression models were fitted to assess associations between the use of online food retail and delivery platforms, and sociodemographic and behavioural factors (including age, sex, household composition, BMI, income adequacy, ethnicity, cooking skills, nutrition knowledge, and frequency of food preparation). RESULTS: Online ordering was more prevalent in Mexico (72%), and in the United States (62%) in comparison with Australia, Canada, or the United Kingdom (45-56%). Overall, across all countries, 58% of participants used online retail and delivery platforms, most commonly online orders from restaurants (36% of participants), online supermarkets (28%), online meal kits (14%), online only grocery stores (11%), and online convenience stores (11%). The odds of using online restaurants was significantly higher for men (OR: 1.23, 95% CI: 1.14-1.33) and participants aged 18-29 (compared to those 60 years or older) (OR: 6.10, 95% CI: 5.34-7.00). Participants aged 18-29 also had the highest odds of using online convenience stores (OR: 7.51, 95% CI: 5.71-9.88). Participants living with primary school aged children had higher odds of using online supermarkets compared to those without children (OR: 2.56, 95% CI: 2.22-2.94). CONCLUSIONS: A substantial proportion of people are buying food online. Efforts to improve population diets need to ensure that online food retail platforms support good health and nutrition.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".