Millennial parents and their children (≤6 years old) \nin fast food restaurants. \nA series of experiments exploring nudging millennial parents into ordering healthier food options for their children in fast-food restaurants.
Bibliographic record
Abstract
The goal of the research was to better understand millennial parents and to investigate options to nudge millennial parents towards the selection of healthier food choices for children (≤ 6 years old), a demographic which to date has had minimal research attention. \nThe focus was on the in-restaurant experience through a series of experiments, which included: \n• Quantitative survey-based research on nudging opportunities through food design, calorie visibility, and pricing. \n• Qualitative cart-sort research on how branding of healthy food options is perceived by children. \n• Multi-country online quantitative research on the mindset of the millennial parent and food motives (Australia, Canada, the UK, and the US). \n• Quantitative observational studies of the family fast-food dining experience and window of influence. \n \nKey findings include the following: \n• The in-restaurant window of opportunity to nudge food choice decisions is very short. With millennial parents’ growing use of technology for ordering food outside of the restaurant environment, technology-based nudging, rather than in-restaurant nudging tools, may prove to be more effective in altering behaviours. \n• Pricing is a possible nudging tool. Punitive rather than incentive pricing appears more financially feasible for restaurant implementation. \n• Increased menu transparency, such as posting calories, may play a role in the development and selection of lower calorie menu items. \n• Toys included in child meal bundles appear to have limited value as an incentive for driving healthy food choices. \n• Children have an early awareness of branding and of what constitutes nutritious food choices. \n• The current fast-food family dining experience includes high levels of technoference, staged eating, and the use of fast-food restaurants as a ‘third place’ (home-away-from-home). Keywords:
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".