Healthy Kai (Food) Checker Web-Based Tool to Support Healthy Food Policy Implementation: Development and Usability Study
Bibliographic record
Abstract
BACKGROUND: Public health programs and policies can positively influence food environments. In 2016, a voluntary National Healthy Food and Drink Policy was released in New Zealand to improve the healthiness of food and drinks for hospital staff and visitors. However, no resources were developed to support policy implementation. OBJECTIVE: This study aimed to design, develop, and test a new web-based tool to support food providers implementing the National Healthy Food and Drink Policy in New Zealand. METHODS: The Double Diamond model, a structured framework with 4 design phases, was used to design and develop a web-based tool. Findings from our previous research, such as (1) systematic review of barriers and facilitators to workplace healthy food policy implementation; (2) scoping review of current tools and resources available in New Zealand, Australia, and Canada; (3) interviews with food providers and public health nutrition professionals; and (4) food and drink availability audit results in New Zealand hospitals were used in the "Discover" (understanding of current gaps) and "Define" (prioritizing functions and features) phases. Subsequent phases focused on generating ideas, creating prototypes, and testing a new web-based tool using Figma, a prototyping tool. During the "Develop" phase, project stakeholders (11 public health nutrition professionals) provided feedback on the basic content outline of the initial low-fidelity prototype. In the final "Deliver" phase, a high-fidelity prototype resembling the appearance and functionality of the final tool was tested with 3 end users (public health nutrition professionals) through interactive interviews, and user suggestions were incorporated to improve the tool. RESULTS: A new digital tool, Healthy Kai (Food) Checker-a searchable database of packaged food and drink products that classifies items according to the Policy's nutritional criteria-was identified as a key tool to support Policy implementation. Of 18 potential functions and features, 11 were prioritized by the study team, including basic and advanced searches for products, sorting list options, the ability to compile a list of selected products, a means to report products missing from the database, and ability to use on different devices. Feedback from interview participants was that the tool was easy to use, was logical to navigate, and had an appealing color scheme. Suggested visual and usability improvements included ensuring that images represented the diverse New Zealand population, reducing unnecessary clickable elements, adding information about the free registration option, and including more frequently asked questions. CONCLUSIONS: Comprehensive research informed the development of a new digital tool to support implementation of the National Healthy Food and Drink Policy. Testing with end users identified features that would further enhance the tool's acceptability and usability. Incorporation of more functions and extending the database to include products classified according to the healthy school lunches program policy in the same database would increase the tool's utility.
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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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".