Exploring Nutrition Labelling of Food and Beverages in Vending Machines in Canadian Recreational Sport Settings
Bibliographic record
Abstract
Purpose: To evaluate whether interpretive “health” labels placed in vending machines in recreation centres represented products’ nutrient content when compared with provincial nutrition guidelines. Methods: A cross-sectional audit (November 2015 – April 2016) of 139 vending machines in recreation facilities found 525 foods and beverages in 17 machines labelled by vendors according to healthfulness. Product nutrient content was compared with provincial nutrition guideline criteria. Cross-tabulation and weighted Cohen’s kappa evaluated agreement between vendor interpretive labels and guideline ranks. Descriptive statistics evaluated how mislabelled products deviated from recommended nutrient content. Mann–Whitney tests compared nutrient content of “healthy” and “unhealthy” labelled products. Results: Almost one-third of all products were mislabelled by vendors with 72% of those labelled healthier than their actual guideline rank. Energy, total fat, sugar, and sodium contents exceeded recommended levels in one-third to one-half of mislabelled products. Overall, products labelled healthy by vendors were significantly lower in energy, sodium, and fat compared with those labelled unhealthy; however, not for all food types (e.g., bars, fruit snacks, nuts). Conclusions: For certain product categories, vendor interpretive nutrition labels poorly represented products’ nutrient content according to provincial nutrition guidelines. Dietitians may be a valuable resource to help implement nutrition guidelines to create credible interpretive product labelling systems.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".