Interventions to reduce sugar‐sweetened beverage consumption using a nudge approach in Victorian community sports settings
Bibliographic record
Abstract
OBJECTIVE: To assess the effectiveness of interventions using a nudge approach to reduce sugar-sweetened beverage purchases in community sports settings. METHODS: A total of 155 community sporting organisations participating in VicHealth funded programs were invited to nominate a nudge based on a traffic light approach to drinks classification. These included limit red drinks, red drinks off display, water the cheapest option, and meal deals. Sales data was collected for a predetermined period prior to and following the introduction of the nudge. Nudges were classified initially on whether they were implemented to VicHealth standards. Appropriately implemented nudges were classified as successful if they achieved a relative decrease in sales from drinks classified as red. RESULTS: In all, 148 organisations trialled 195 nudges; 15 (7.7%) were successful and 20 (10.3%) were appropriately implemented but unsuccessful. Limit red drinks was the most frequently attempted nudge (30.8%). Red drinks off display had the greatest rate of success (20.0%). CONCLUSIONS: Red drinks off display was the simplest and most successful nudge. Implications for public health: Guidelines limiting the display of sugar-sweetened beverages may be an effective means of altering consumer behaviour.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".