Special diets in modern America: Analysis of the 2012 National Health Interview Survey data
Bibliographic record
Abstract
BACKGROUND: Special diets are frequently used by the public but reasons for use and characteristics of users remain unclear. AIM: To determine prevalence of the use of special diets, the individual characteristics associated with their use and reasons for use. METHODS: The secondary analysis used data from the 2012 National Health Interview Survey (NHIS), a cross-sectional household interview survey of a nationally representative sample of non-hospitalized US adult populations ( n = 34,525). The dependent variables in this secondary analysis were the use of a special diet (vegetarian, macrobiotic, Atkins, Pritikin, and Ornish) ever and during the past 12 months. Independent variables included sociodemographic, clinical and behavioral variables. Prevalence of special diet use and reasons for use were analyzed descriptively. Associations between independent and dependent variables were analyzed using Chi-square tests and logistic regression. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. RESULTS: Lifetime and 12-month prevalence of using special diets were 7.5% (weighted n = 17.7 million) and 2.9% (weighted n = 6.9 million), respectively. Individuals using special diets in the past 12 months were more likely female (OR = 1.45; 95% CI = 1.21-1.74), not married (OR = 0.76; 95% CI = 0.63-0.91), college-educated (OR = 1.98; 95% CI = 1.25-3.11) and depressed (OR = 1.50; 95% CI = 1.14-1.98). They more likely also used herbal products (OR = 2.35; 95%CI = 1.84-2.99), non-vitamin (OR = 1.82; 95% CI = 1.45-2.27) and vitamin supplements (OR = 1.57; 95% CI = 1.24-1.99). Diets were mainly used to improve overall health (76.7%) or for general wellness/prevention (70.4%). CONCLUSIONS: Special diets are mainly used for unspecific health reasons by those who are females, have a college degree or with depression, and commonly used in conjunction with herbs and dietary supplements.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".