<i>Diet Quality Among Older Quebecers</i>As Assessed By Simple Indicators
Bibliographic record
Abstract
To determine whether older Quebecers are eating adequately and whether summary scores represent diet quality, a representative subset of participants aged 55 to 74 (weighted n=460, 47% male) was studied from the 1990 Enquête québécoise sur la nutrition dataset. Participants' diet quality was scored from adjusted 24-hour recalls. Foods were coded into Canada's Food Guide to Healthy Eating food groups. Usual Dietary Adequacy Score (maximum=18) and Dietary Diversity Score (maximum=4) were calculated from adjusted food guide portions and validated internally in relation to achievement of nutrient recommendations using correlation analysis. Average usual Dietary Adequacy Score (mean +/- standard error) was 14.96 +/- 0.15 (men) and 13.72 +/- 0.15 (women). Only 7% of men and 1% of women achieved the maximum usual score. Forty-four percent of men and 45% of women scored a usual Dietary Diversity Score of 3, and 55% of men and 50% of women achieved 4. Thus, approximately half of older Quebecers showed inadequate dietary variety, and consumed fewer than the minimum recommended number of servings from certain food groups. Summary diet quality indicators are useful for tracking diet quality, and provide critical data for planning nutrition education programs targeting older persons.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".