Bibliographic record
Abstract
Purpose Food guides are graphic representations of food-based dietary guidelines that support national health policies and programming. They are visual aids simplifying complex nutritional messaging for the public. While pyramid and circle formats are the most common shapes in use worldwide, the dinner plate format is increasing in use due to its perceived effectiveness. However, research examining visual attributes of food guide graphics, and the dinner plate model specifically, is limited. The purpose of this paper is to systematically compare and analyse key visual attributes of plate food guide graphics (across multiple examples) to assess their potential for effective visual communication of nutrition messaging. Design/methodology/approach This study engages in a qualitative analysis of compositional elements of food guide graphics. Data collection and analysis are grounded in the methods of compositional interpretation, which includes a qualitative, descriptive approach to establishing a thematic survey of the data. Findings Unique visual attributes of the plate food guide (including image content, spatial organisation and expressive content) present challenges in the communication of key nutritional messaging regarding proportionality, moderation and overall usability. Practical implications A better understanding of the visual attributes of the plate food guide model will contribute to improved design and development of this key public health tool by researchers, educators and health practitioners. Additionally, the examination of visual attributes has implications for the study of food guide understanding and use. Originality/value This study highlights the need for critical visual skills in qualitative health research, and to address gaps in health education more broadly.
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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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".