Recall of government healthy eating campaigns by consumers in five countries
Bibliographic record
Abstract
OBJECTIVE: To examine awareness and recall of healthy eating public education campaigns in five countries. DESIGN: Data were cross-sectional and collected as part of the 2018 International Food Policy Study. Respondents were asked whether they had seen government healthy eating campaigns in the past year; if yes (awareness), they were asked to describe the campaign. Open-ended descriptions were coded to indicate recall of specific campaigns. Logistic models regressed awareness of healthy eating campaigns on participant country, age, sex, ethnicity, education, income adequacy and BMI. Analyses were also stratified by country. SETTING: Online surveys. PARTICIPANTS: Participants were Nielsen panelists aged ≥18 years in Australia, Canada, Mexico, UK and the USA (n 22 463). RESULTS: Odds of campaign awareness were higher in Mexico (50·9 %) than UK (18·2 %), Australia (17·9 %), the USA (13·0 %) and Canada (10·2 %) (P < 0·001). Awareness was also higher in UK and Australia v. Canada and the USA, and the USA v. Canada (P < 0·001). Overall, awareness was higher among males v. females and respondents with medium or high v. low education (P < 0·001 for all). Similar results were found in stratified models, although no sex difference was observed in Australia or UK (P > 0·05), and age was associated with campaign awareness in UK (P < 0·001). Common keywords in all countries included sugar/sugary drinks, fruits and vegetables, and physical activity. The top five campaigns recalled were Chécate, mídete, muévete (Mexico), PrevenIMSS (Mexico), Change4Life (UK), LiveLighter® (Australia), and Actívate, Vive Mejor (Mexico). CONCLUSIONS: In Mexico, UK and Australia, comprehensive campaigns to promote healthy lifestyles appear to have achieved broad, population-level reach.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".