Offline to online: a systematic mapping review of evidence to inform nutrition-related policies applicable to online food delivery platforms
Bibliographic record
Abstract
BACKGROUND: Online food delivery (OFD) platforms offer easy access to an abundance of energy-dense and nutrient-poor takeaway foods and may exacerbate existing unhealthy food environments. Efforts to improve population diets include a range of policy recommendations focused on improving the healthiness of food environments; however, the way in which such policies may apply to OFD platforms is not clear. This paper aimed to synthesise the existing evidence to inform nutrition-related policies applicable to OFD platforms for population health and well-being. A secondary aim was to scan existing nutrition-related policies in Australia and internationally, which have the potential to be applicable to OFD platforms. METHODS: Seven electronic databases including Medline, Embase, CINAHL, Business Source Ultimate, Scopus, Web of Science, and Proquest were searched from January 2010 to October 2023. Evidence from studies was mapped to five existing policy domains outlined by the Healthy Food Environment Policy Index (Food-EPI) including (i) food labelling; (ii) food promotion; (iii) food composition and nutritional quality; (iv) food retail; and (v) food pricing. Relevant data sources were searched for currently implemented nutrition-related government policies that may have relevance to OFD platforms. RESULTS: A total of 2012 records were screened, and 43 studies were included. There were 70 relevant study outcomes across the included studies, which addressed one or more of the 5 domains. Of these, 21 were relevant to 'Food Promotion' (30%), 18 to 'Food Retail' (26%), 15 to 'Food Composition (21%), 11 to 'Food Prices' (16%), and six to 'Food Labelling' (9%). Three existing policies from international jurisdictions (England, Singapore, EU) included OFD platforms, of which one was a voluntary measure. Several existing policies under food labelling have the potential to be amended to include OFD platforms under regulatory definitions. CONCLUSION: OFD platforms have emerged as a disruptor to how people acquire their food and have yet to be widely included in existing nutrition-related policies. Advancing the evidence base to support the design of effective policy actions and mitigate the potential negative health impacts of OFD platforms will support efforts to improve population diets.
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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 | 0.021 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.030 | 0.033 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".