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Record W4292452108 · doi:10.1177/08982643221118445

Respiratory Health Among Older Adults in Vietnam: Does Earlier-Life Military Role and War Exposure Matter?

2022· article· en· W4292452108 on OpenAlexaff
Bussarawan Teerawichitchainan, Zachary Zimmer, Timothy Qing Ying Low, Trần Khánh Toàn

Bibliographic record

VenueJournal of Aging and Health · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsMount Saint Vincent University
FundersNational Institute on Aging
KeywordsVietnam WarGerontologyPsychologyEnvironmental healthMedicinePolitical science

Abstract

fetched live from OpenAlex

Objective We explore how earlier-life military roles and war trauma associate with later-life respiratory health in Vietnam. Method: The population-based sample aged 60+ is from the 2018 Vietnam Health and Aging Study. Poisson and binary logistic regressions investigate correlates of overall lung health, measured as total number of four conditions, and individual conditions, with focus on earlier-life wartime experiences. Results: Exposure is associated with lung conditions. Overall, a one-standard deviation increase in exposure results in 0.529 more conditions ( p ≤ .001). Association varies across military roles and is partially explained by PTSD and smoking. Civilians heavily exposed to war trauma exhibit worse lung health than similarly exposed formal and informal military personnel. Discussion: Earlier-life war exposure is an important predictor of late-adulthood respiratory health in lower- and middle-income countries. Evidence calls for attention to the long-term impacts of war on health among not only formal and informal military personnel but also civilians.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.522

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.293
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2022
Admission routes1
Has abstractyes

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