The choice of a diet quality indicator to evaluate the nutritional health of populations
Bibliographic record
Abstract
BACKGROUND: The USA and Canada both want to reduce social health inequalities in their population. These two countries have recently begun a process of harmonization of their nutrient recommendations. OBJECTIVE: To develop a standardized indicator to measure the impact of these recommendations on the health of different social groups in North America. The authors have compared three of the methods currently used for measuring overall diet quality for a population. DESIGN AND SETTING: The three methods, adjusted to the 1990 Canadian nutrition recommendations, were used to analyse the Québec Nutrition Survey data collected by Santé Québec in 1990. RESULTS: The authors found that the indicator developed by Kennedy and collaborators works best for analysing the Québec data. Moreover, it allows comparisons with the USA. Some questions, such as whether or not to add calories from alcohol consumption to the model and whether the indicators should be adjusted to the different cultures and specific population groups remain unanswered. CONCLUSIONS: In order to determine the role of nutrition in social health inequalities, it is important to develop standard indicators that are suitable for monitoring the relationship between dietary recommendations and eating habits.
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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 | 0.035 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".