War across the life course: examining the impact of exposure to conflict on a comprehensive inventory of health measures in an aging Vietnamese population
Bibliographic record
Abstract
BACKGROUND: The majority of evidence indicates that exposure to war and other traumatic events continue to have negative impacts on health across the life course. However, existing research on health effects of war exposure primarily concentrates on short-term impacts among veterans in high-income countries sent elsewhere to battle. Yet, most wars situate in lower- and middle-income countries, where many are now or will soon be entering old age. Consequently, the current burden of exposure to war has ignored an important global population. METHODS: The Vietnam Health and Aging Study (VHAS) is a longitudinal study designed to examine historical exposure to highly stressful events during the American War. Two modes of data collection, involving a sample of 2447 individuals aged 60+ years in northern Vietnam, took place between May and August 2018. Using this first wave of data, we generate indexed measures of war exposure and analyze their associations with a set of 12 health outcomes, accounting for confounding variables. RESULTS: Results indicate that greater exposure to three types of war exposure (death and injury, stressful living conditions, and fearing death and/or injury) in earlier life is associated with worse health in later-life across a large number of health outcomes, such as number of diagnosed health conditions, mental distress, somatic symptoms, physical functioning, post-traumatic stress symptoms and chronic pain. CONCLUSIONS: Findings support a life course theory of health and point to long-term effects of war on health that require detailed attention.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".