Patterns of productive aging among Vietnamese war survivors: The influential role of early-life war exposure and past military service
Bibliographic record
Abstract
OBJECTIVES: War disrupts millions of lives, particularly in low- and middle-income countries (LMICs), where armed conflicts are most frequent. Although war has received extensive research attention, 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 gender. METHODS: Data come from the Vietnam Health and Aging Study (N = 2,447 war survivors aged 60+). Latent class analysis identifies patterns of later-life engagement across five domains: work, in-kind support, caregiving, community involvement, and self-development. Multinomial logistic regression analyses assess the associations between war exposure, military role, gender, and productive aging profiles. RESULTS: The analysis identifies five engagement patterns: High Engagers, Altruistic, Low Engagers, Civic Developers, and Active Workers. Greater war exposure is associated with profiles reflecting higher levels of engagement. Military role moderates this relationship: formal military veterans are more likely to be classified as Active Workers under high exposure conditions. Informal military members, by comparison, are more likely to be classified as Low Engagers. Gender further moderates these patterns, with women less likely to belong to profiles marked by high levels of public participation. DISCUSSION: To promote productive aging in conflict-affected LMICs, policymakers should consider the long-term effects of early-life war exposure, past military service, and gendered disparities. By addressing these inequalities through early-life interventions, we can potentially improve long-term engagement outcomes.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".