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Record W2124201254 · doi:10.1093/ije/dyq132

War-related stress exposure and mortality: a meta-analysis

2010· review· en· W2124201254 on OpenAlexaff
David J. Roelfs, E. Shor, Karina W. Davidson, Joseph E. Schwartz

Bibliographic record

VenueInternational Journal of Epidemiology · 2010
Typereview
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsMcGill University
FundersNational Heart, Lung, and Blood Institute
KeywordsHazard ratioConfidence intervalDemographyMedicineMeta-analysisObservational studyEpidemiologyGerontologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Domestic and international wars continue to be pervasive in the 21st century. This study summarizes the effects of war-related stress on all-cause mortality using meta-analyses and meta-regressions. METHODS: A keyword search was performed, supplemented by extensive iterative hand-searches for observational studies of war-related stress and mortality. Two hundred and twenty mortality risk estimates from 30 studies were extracted, providing data on more than 9 million persons. RESULTS: The mean hazard ratio (HR) was 1.05 [95% confidence interval (CI) 0.98-1.13] among HRs adjusted for age and additional covariates. The mean effect for men was 1.14 (CI 1.00-1.31), and for women it was 0.92 (CI 0.66-1.28). No differences were found for various follow-up durations or for various types of war stress. Neither civilians nor military personnel had an elevated mortality risk. Those exposed to a combat zone during the Vietnam War had a slightly higher chance of death (HR 1.11; 95% CI 1.00-1.23). CONCLUSIONS: The results show that, over all, exposure to war-stress did not increase the risk of death when studies were well controlled. Effects were small when found. This lack of substantial effect may be the result of selection processes, developed resiliency and/or institutional support.

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)0.006
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.676
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0070.005
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.000

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 head0.453
GPT teacher head0.561
Teacher spread0.108 · 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.

Study designMeta-analysis
Domainnot available
GenreReview

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

Citations27
Published2010
Admission routes1
Has abstractyes

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