Adaptation and validation of a tool for assessing food knowledge based on the Nova classification for the Brazilian context
Bibliographic record
Abstract
OBJECTIVES: To adapt and validate a tool to measure the level of food knowledge based on the Nova classification for the Brazilian context. METHODS: A tool developed by Canadian researchers was adapted for Brazil. In this tool, respondents assign healthiness scores to 12 images of foods with different levels of industrial processing according to the Nova classification - unprocessed and minimally processed, processed and ultra-processed. Total score is computed by comparing scores assigned to foods from different groups, and range from 0 to 8. The Brazilian version, named Nova-Conhecimento, was evaluated by experts and submitted to pre-tests with potential users. Discriminant validity was assessed by comparing scores of undergraduate students of nutrition and undergraduate students in education-related fields. Convergent validity was assessed by testing the association between the knowledge score and the consumption of ultra-processed foods in a subsample of the NutriNet Brazil cohort (n=1,245). RESULTS: Nutrition students had higher scores than education students (6.7 vs. 5.3; p-value<0.001). Each point in the knowledge score was associated with a reduction of 1.03 percentage points in the contribution of ultra-processed foods to the diet (p-value<0.001). CONCLUSION: The Nova-Conhecimento tool demonstrated validity and can contribute to food and nutritional surveillance activities based on the the Dietary Guidelines for the Brazilian Population.
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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".