Soldier use of dietary supplements, including protein and body building supplements, in a combat zone is different than use in garrison
Bibliographic record
Abstract
United States Army personnel in garrison who are not deployed to combat theater report using dietary supplements (DSs) to promote health, increase physical and mental strength, and improve energy levels. Given the substantial physical and cognitive demands of combat, DS use may increase during deployment. This study compared DS use by garrison soldiers with DS use by personnel deployed to a combat theater in Afghanistan. Prevalence and patterns of DS use, demographic factors, and health behaviors were assessed by survey (deployed n = 221; garrison n = 1001). Eighty-two percent of deployed and 74% of garrison soldiers used DSs ≥ 1 time·week(-1). Logistic regression analyses, adjusted for significant demographic and health predictors of DS use, showed deployed personnel were more likely than garrison soldiers to use protein, amino acids, and combination products. Deployed females were more likely to use protein supplements and deployed males were more likely to use multivitamins, combination products, protein, and body building supplements than garrison respondents. Significantly more deployed (17%) than garrison (10%) personnel spent more than $50∙month(-1) on DSs. Higher protein supplement use among deployed personnel was associated with higher frequency of strength training and lower amounts of aerobic exercise for males but similar amounts of strength training and aerobic exercise for females. Protein supplements and combination products are used more frequently by deployed than garrison soldiers with the intent of enhancing strength and energy.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".