Characteristics of consumers using ‘better for you’ front-of-pack food labelling schemes – an example from the Australian Heart Foundation Tick
Bibliographic record
Abstract
OBJECTIVE: The Heart Foundation Tick aims to help consumers make healthier food choices and overcome confusion in understanding food labels. Little is known about what factors differentiate frequent from infrequent users and the effectiveness of this scheme in helping Australians make healthier food choices. DESIGN: A cross-sectional survey was used to explore use of the Tick and associations with a range of individual characteristics. SETTING: A national panel of Australians, living in each state and territory, completed an online survey (n 1446). SUBJECTS: Adult men (41 %) and women participated in the study. RESULTS: Most trusted the Heart Foundation (79 %), and used the Tick at least occasionally (19 % regularly, 21 % often, 35 % occasionally, 24 % never). A majority was classified as overweight/obese (60 %), 3·5 % were diagnosed with CHD, 5·2 % with diabetes and 23 % with hypertension. Many did not meet recommendations for the consumption of red meat (30 %), processed meat (23 %), vegetables (78 %), fruit (43 %) and fast foods (47 %). Female frequent users tended to have hypertension, be married/de facto, older than 45 years, rural dwellers, and limit their intake of fast foods. Male frequent users tended to have hypertension, meet recommendations for fruit, vegetables and processed meats, but not have a tertiary education. CONCLUSIONS: The Heart Foundation Tick is a highly trusted, highly recognizable food labelling scheme and helpful to consumers who are motivated to make healthier food choices. More inter-sector collaboration is required to incorporate these schemes into public health campaigns to help consumers make healthier food choices.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".