The efficacy of ‘high in’ warning labels, health star and traffic light front-of-package labelling: an online randomised control trial
Bibliographic record
Abstract
OBJECTIVE: To examine the impact of front-of-package (FOP) labels on perceived healthfulness, purchasing intentions and understanding of common FOP systems. DESIGN: A parallel, open-label design randomised participants to different FOP labelling conditions: 'high in' warning labels (WL), multiple traffic light labelling (TLL), health star ratings (HSR) (all displayed per serving) or control with no interpretive FOP labelling. Participants completed a brief educational session via a smartphone application and two experimental tasks. In Task 1, participants viewed healthy or unhealthy versions of four products and rated healthiness and purchasing intention on a seven-point Likert-type scale. In Task 2, participants ranked three sets of five products from healthiest to least healthy. SETTING: Online commercial panel. PARTICIPANTS: Canadian residents ≥ 18 years who were involved in household grocery shopping, owned a smartphone and met minimum screen requirements. RESULTS: Data from 1997 participants (n 500/condition) were analysed. Task 1: across most product categories, the TLL and HSR increased perceived healthiness of healthier products. All FOP systems decreased perceived healthiness of less healthy products. Similar, albeit dampened, effects were seen regarding purchasing intentions. Task 2: participants performed best in the HSR, followed by the TLL, WL and control conditions. Lower health literacy was associated with higher perceived healthiness and purchasing intentions and poorer ranking task performance across all conditions. CONCLUSIONS: All FOP labelling systems, after a brief educational session, improved task performance across a wide spectrum of foods. This effect differed depending on the nutritional quality of the products and the information communicated on labels.Trial Registration: NCT03290118.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".