Longitudinal association of health behaviors and health-related quality of life with military spouse readiness
Bibliographic record
Abstract
BACKGROUND: Unhealthy behaviors impose costs on health-related quality of life (HRQOL) reducing productivity and readiness among military members (Hoge et al., JAMA 295:1023-32, 2006; Mansfield et al. 362:101-9, 2010). Among married personnel in particular, patterns of spouse health behaviors may play an interdependent role. As a result, the identification of military spouse health factors related to readiness may inform strategies to screen for and identify those in need of greater support and enhance readiness. This study explored behavioral and HRQOL predictors and potential mediators of military spouse readiness utilizing data from the Millennium Cohort Family Study. METHODS: The analytic sample comprised of 3257 spouses of active-duty, non-separated service members who responded to both waves 1 and 2 of the survey. Sample characteristics are described with respect to demographics (e.g., age, sex, race/ethnicity, etc.), readiness measures (i.e., military satisfaction, lost workdays, health care utilization, military-related stress, and satisfaction), health behaviors (i.e., exercise, sleep, smoking, and alcohol use) and HRQOL (Veterans RAND 12-Item Short Form Survey). We conducted multivariate mediation analyses to evaluate the role of mental and physical HRQOL as mediators between the baseline health behaviors and the health readiness outcomes at follow-up, while adjusting for spouse and service member demographics. RESULTS: HRQOL had direct effects for all five readiness outcomes examined. Multiple health behaviors (insomnia, smoking, binge drinking, and exercise) were further significantly associated with spouse readiness outcomes, although most effects were mediated through HRQOL, suggesting this may be a useful index of military spouse readiness. Insomnia was the specific health behavior most consistently associated with poorer readiness across outcomes, and effects were only partially mediated by physical and mental HRQOL. CONCLUSIONS: The results show spouse health behaviors are directly and indirectly (through HRQOL) associated with readiness indicators. This suggests that assessments of modifiable health behaviors (e.g., insomnia symptoms) and mental and physical HRQOL are important indicators of readiness among military spouses and should be used to inform future programs designed to improve population health.
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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 | 0.006 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".