Intersections of Educational Attainment, Indigenous Identity, and Race/Ethnicity Best Predicted Diet Quality Among Adults in Canada: A Conditional Random Forests Analysis
Bibliographic record
Abstract
BACKGROUND: Although it is well-known that diet quality varies according to multiple dimensions of socioeconomic position (SEP), much remains unknown about how these dimensions together shape diet quality. Given that diet quality associated with 1 SEP dimension (eg, income) can systematically differ across another dimension (eg, race and ethnicity), it is necessary to investigate diet quality across SEP intersections. OBJECTIVES: The aim of this study was to identify SEP intersections that best predicted lower and higher diet quality among adults in Canada. DESIGN: Population-based data were from the cross-sectional 2015 Canadian Community Health Survey-Nutrition. Data were collected by interviewers who visited selected dwellings to collect household information and administer a general health questionnaire and a 24-hour dietary recall. PARTICIPANTS/SETTING: Data from 13 617 adults aged 18 years and older living in Canada's 10 provinces. MAIN OUTCOME MEASURES: Twenty-four-hour dietary recall data were used to assess diet quality based on the Healthy Eating Index-2015 (HEI-2015) score (range, 0 to 100). STATISTICAL ANALYSES PERFORMED: Conditional random forests, a supervised machine-learning technique, were used to identify 4 of 12 SEP indicators that best individually predicted HEI-2015 scores. The resulting 4 most important predictors were used to predict diet quality using all possible 2-way intersections. RESULTS: The 4 most important intersectional predictors of HEI-2015 scores based on conditional random forest variable importance measures were (1) educational attainment and Indigenous identity and race/ethnicity, (2) educational attainment and household food insecurity, (3) educational attainment and sex/gender, and (4) household food insecurity and sex/gender. Among these 4 SEP intersections, individuals without a high school diploma living in a severely food-insecure household had the lowest (55.7), and individuals without a high school diploma identifying as Middle Eastern had the highest (64.5) predicted HEI-2015 scores. CONCLUSIONS: The SEP intersection defined by educational attainment and Indigenous identity and race/ethnicity was the most important predictor 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".