The physical and mental health of a large military cohort: baseline functional health status of the Millennium Cohort
Bibliographic record
Abstract
BACKGROUND: The US military is currently involved in large, lengthy, and complex combat operations around the world. Effective military operations require optimal health of deployed service members, and both mental and physical health can be affected by military operations. METHODS: Baseline data were collected from 77,047 US service members during 2001-2003 as part of a large, longitudinal, population-based military health study (the Millennium Cohort Study). The authors calculated unadjusted, adjusted, and weighted means for the Medical Outcomes Study Short Form 36-item Survey for Veterans physical (PCS) and mental component summary (MCS) scores over a variety of demographic and military characteristics at baseline. RESULTS: The unadjusted mean PCS and MCS scores for this study were 53.4 (95% confidence interval: 53.3-53.4) and 52.8 (95% confidence interval: 52.7-52.9). Average PCS and MCS scores were slightly more favorable in this military sample compared to those of the US general population of the same age and sex. Factors independently associated with more favorable health status included male gender, being married, higher educational attainment, higher military rank, and Air Force service. Combat specialists had similar health status compared to other military occupations. Having been deployed to Southwest Asia, Bosnia, or Kosovo between 1998 and 2000 was not associated with diminished health status. CONCLUSION: The baseline health status of this large population-based military cohort is better than that of the US general population of the same age and sex distribution over the same time period, especially in older age groups. Deployment experiences during the period of 1998-2001 were not associated with decreased health status. These data will serve as a useful reference for other military health studies and for future longitudinal analyses.
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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) | 0.000 | 0.000 |
| 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) | 0.001 | 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".