Healthy food labeling on UBC campus : written report
Bibliographic record
Abstract
In Canada, the economic burden of poor dietary habits is estimated at $6.3 billion annually1. At the University of British Columbia (UBC), nutrition labeling has been identified as a key action area to improve student dietary habits and health (L. McGowan, oral communication, 2014). Excessive consumption of total fat, added sugar, sodium and calories place individuals at a higher risk for obesity2, hypertension3, type 2 diabetes4 and heart disease5. Due to a high level of interest from students, it is an appropriate time to implement a user-friendly food labeling system to help students to choose healthier food options. The goal of our proposed UBC Healthy Food Labels (HFL) is to decrease the prevalence of hypertension and obesity, as well as the risks for type 2 diabetes and heart disease in UBC students over the next four years. To decrease the consumption of foods high in total fat, added sugar, sodium, and calories among UBC students dining on campus, we propose modeling the UBC HFL after the United Kingdom (UK) Traffic Light Signpost Labeling System (TLSLS), an efficient, user-friendly, consumer-preferred method of communicating nutrition information6. Our proposed criteria for the UBC HFL are tailored to fit the UBC student population in the context of Canadian nutrition guidelines. By September 2014, we hope the community partners will approve this labeling system and use it to build upon the existing “Rez Allergen Checklist” labels, which identify common food allergens and/or intolerances. By addressing qualities that affect the speed and extent that UBC HFL will successfully spread throughout the UBC campus, we want to see at least 50% of students using the UBC HFL to decrease their total fat, added sugar, sodium and caloric intake by the end of April 2016. While a process evaluation is used throughout the development of the project, an impact evaluation will be used to determine community partners’ approval of the proposed UBC HFL using a Likert scale and the UBC HFL’s effect on promoting healthy eating among UBC students using a campus-wide survey and review of food sale patterns. Overall, we are confident that by using TLSLS as a model for the UBC HFL we can reach our goal. Disclaimer: “UBC SEEDS provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student project/report and is not an official document of UBC. Furthermore readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or the SEEDS Coordinator about the current status of the subject matter of a project/report.”
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.024 |
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".