Productive Aging Profiles Among Vietnamese War Survivors: The Role of Early-Life War Exposure and Military Service
Bibliographic record
Abstract
Abstract War disrupts millions of lives, particularly in low- and middle-income countries (LMICs), where armed conflicts are most frequent. While much is known about war’s detrimental effects on physical and psychological health, its long-term impact on productive aging remains unclear. This study investigates how early-life war exposure affects productive aging among older Vietnamese war survivors and examines the moderating role of past military service and war trauma exposure. Data come from the Vietnam Health and Aging Study (N = 2,447 war survivors aged 60+). Latent class analysis identifies patterns of engagement across five domains: work, in-kind support, caregiving, community involvement, and self-development. Multinomial logistic regressions assess relationships between war exposure intensity, military role, and engagement patterns. Our analyses identify five patterns of later-life productive engagement: High Engagers, Altruistic, Low Engagers, Civic Developers, and Active Workers. Greater exposure intensity is associated with profiles indicative of higher levels of engagement. Military roles moderate this relationship, with formal military veterans more likely to belong to the Active Workers class under high exposure conditions, while informal military members are more likely to be classified as Low Engagers. To promote productive aging in conflict-affected LMICs, policymakers should consider the long-term impacts of early-life war exposure and past military service. Addressing inequalities and introducing interventions earlier in the life course could enhance effectiveness.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".