<i>Paint Your Plate:</i> Effectiveness of a Point-of-purchase Display
Bibliographic record
Abstract
PURPOSE: This study was conducted to determine consumer understanding and retention of nutrition information presented at grocery stores during the Paint Your Plate campaign via two approaches: interactive display events and brochure distribution. METHODS: Data were collected at 17 grocery stores in northern Ontario. Eleven stores held interactive display events with public health staff, a display, resources, and food samples. Six stores only distributed brochures. A total of 688 participants completed a baseline questionnaire, and 432 consented to a three-month follow-up telephone call. Of these, 201 were randomly selected to participate. RESULTS: Participants at interactive display events were six times more likely than participants receiving brochures to identify a serving size of fruit and vegetables (odds ratio [OR]=5.88; 95% confidence interval [CI]: 4.05, 8.54) and 23 times more likely to identify the recommended number of servings of fruit and vegetables (OR=22.67; 95% CI: 14.29, 35.98). However, at follow-up, there was no significant difference between type of event and the ability to answer correctly. CONCLUSIONS: Interactive displays increased immediate knowledge but failed to increase retention, a finding that suggests consistent presence of the message is needed to reinforce initial understanding and retention. More emphasis should be placed on directing funding toward increasing the frequency and duration of promotional efforts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".