Longitudinal trends in use of dietary supplements by U.S. Army personnel differ from those of civilians
Bibliographic record
Abstract
Prevalence and patterns of dietary supplement (DS) use by U.S. Army soldiers differ from the civilian population. Longitudinal trends in use of DSs by civilians have been examined, but are unavailable in subpopulations such as military service members. The present study examined longitudinal changes in DS use by soldiers. A standardized questionnaire on DS use was administered in 2006–2007 (N = 989) and 2010–2011 (N = 1196) to convenience samples of active duty soldiers. Data were weighted for total population demographics of age, sex, and rank. Regular use of DSs by soldiers increased significantly (56% ± 1.6% vs. 64% ± 1.7%; p ≤ 0.001) over the 4 years primarily because of an increase of DS use among the youngest 18- to 24-year-old soldiers (43.0% ± 2.5% vs 62.3% ± 2.4%; p ≤ 0.01). Protein (22% ± 1.4% vs. 26% ± 1.5%; p ≤ 0.001) and combination (10.0% ± 1.0% vs. 24% ± 1.4%; p ≤ 0.001) product consumption also increased over the 4 years. Individual vitamin and mineral use — including iron, magnesium, selenium, and vitamins A, B6, B12, and D — significantly increased as well (p ≤ 0.05). In addition, expenditures on DSs by soldiers increased over time (p < 0.01). Reasons reported by soldiers for DS use suggest use increased to meet the occupational demands of military service. Educational interventions to minimize inappropriate use of DSs by soldiers are necessary to reduce adverse events resulting from unnecessary use of DSs and the financial burden associated with their use.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".