Baseline determinants of global diet quality in older men and women from the NuAge cohort
Bibliographic record
Abstract
UNLABELLED: Judicious food choices are of prime importance during aging. OBJECTIVES: This study was conducted to identify individual and collective attributes determining global diet quality (DQ). METHODOLOGY: Participants were 1,793 adults (52% women) from the NuAge study on nutrition and successful aging. Subjects aged 67 to 84 years in relatively good health were recruited from the Québec Medicare Database. Sociodemographic, affective, and cognitive data, health conditions, perceived physical health and functional status, dietary habits and dietary attributes and community resources were obtained using questionnaires. Body weight and height were measured and body mass index (BMI) was calculated. Three non-consecutive 24-hour diet recalls were collected at recruitment. DQ, assessed using the Canadian Healthy Eating Index (C-HEI, /100), was computed on the mean intakes from the diet recalls. Analyses were stratified by gender. Variables significantly related to DQ in bivariate analyses (p<.05) were entered into backward stepwise multiple regression analyses. RESULTS: Among men, the final model showed higher education (β=0.23, p=.01), diet knowledge (β=0.96, p<.0001), number of daily meals (β=1.91, p=.02) and perceived physical health (β=0.06, p=.01) to be positive determinants of DQ, whereas alcohol consumption (β=-2.25, p=.05), wearing dentures (β=-2.31, p=.01) and eating regularly in restaurants (β=-1.65, p=.03) were negative determinants of DQ (adjusted R2 = 13.7%). Among women, higher education (β=0.29, p=.002), diet knowledge (β=0.54, p=.002), number of daily meals (β=3.61, p<.0001), and hunger (β=0.61, p<.0001) were positive determinants of global DQ; greater BMI (β=-0.16, p=.03) and chewing problems (β=-0.48, p=.03) were negative determinants of DQ (adjusted R2 = 7.8%). DISCUSSION: These results point to several key factors influencing global DQ in older adults and also show gender-based differences. More research must be done to better understand how these factors change with aging and exert their impact on diet, particularly since variance in DQ was largely unexplained. As diet knowledge was an independent predictor for both genders, targeted, sustainable interventions are needed to ensure good diet quality as people age.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".