Behavioral factors are perhaps more important than income in determining diet quality in Canada
Bibliographic record
Abstract
This study examines the importance of income in determining the diet quality of Canadian adults measured based on Nutrient Rich Food Index version 9.3. We used the latest available data on Canadians' consumption of foods and nutrients from the Canadian Community Health Survey-Nutrition 2015. The Canada' Food Guide classification was used for categorizing food groups based on types of food and their healthiness. Unsupervised and supervised machine learning models were employed in order to examine the links between income and the choice of foods. We first employed cluster analysis to identify the dietary patterns among individuals included in the sample and then we examined whether the intakes of various food groups across the identified clusters vary by income levels. Further, we evaluated the association between diet quality and income using Lasso Regression to determine the most important predictors of diet quality among adults in Canada. The results of both cluster analysis and regularized regression model suggested that behavioral factors and cultural backgrounds are more important determinants of diet quality among adults in Canada.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".