Prevalence of vitamin-mineral supplement use and associated factors among Canadians: results from the 2015 Canadian Community Health Survey
Bibliographic record
Abstract
Vitamin/mineral supplements are used for improving micronutrient intake and preventing deficiencies, particularly for shortfall nutrients. We assessed the prevalence of vitamin/mineral supplement use and associated factors among a representative sample of Canadians aged ≥1 years. We used nationally representative data from the 2015 Canadian Community Health Survey (CCHS)-Nutrition. The prevalence of vitamin/mineral supplement use containing shortfall nutrients (vitamins: A, C, D, B6, B12 and folate; minerals: calcium, magnesium, and zinc) was examined in this study. Logistic regression models were performed to determine factors associated with vitamin/mineral supplement use among Canadian children (1–18 years) and adults (>19 years). The overall prevalence of vitamin/mineral supplement use was 38% among men and 53% among women. Males aged 14–18 years had the lowest prevalence (26.5%; 95% confidence interval (CI) = 21.9–31.0) and females aged ≥71 years had the highest prevalence (67.8%; 95% CI = 64.1–71.5) of vitamin/mineral supplement use. Female gender, older age, higher education level, higher income, living in urban areas, having chronic conditions, having a normal body mass index (BMI), and being non-smoker were independent positive predictors of vitamin/mineral supplement use among adults. Independent positive predictors of vitamin/mineral supplement use among Canadian children included younger age, having a normal BMI, and being food secure. Novelty: The overall prevalence of vitamin/mineral supplement use among Canadian men and women was 38% and 53%, respectively. Sociodemographic and lifestyle variables were associated with vitamin/mineral supplement use, especially among Canadian adults.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".