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Record W2111162900 · doi:10.1177/0190272513513962

The Threat of War and Psychological Distress Among Civilians Working in Iraq and Afghanistan

2014· article· en· W2111162900 on OpenAlexaff
Alex Bierman, Ryan Kelty

Bibliographic record

VenueSocial Psychology Quarterly · 2014
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsStressorPsychologyDistressMental healthSocial psychologyTollConstraint (computer-aided design)Psychological distressMilitary personnelDevelopmental psychologyClinical psychologyPsychiatryPolitical scienceMedicine

Abstract

fetched live from OpenAlex

Research documents the mental health toll of combat operations on military personnel in Iraq and Afghanistan, but little research examines civilians who work alongside members of the military. In this research, we argue that a sense of threat is an “ambient stressor” that permeates daily life among civilians who work in these war zones, with mastery likely to both mediate and moderate the mental health effects of this stressor. Using a unique probability sample of Department of Army civilians, we find that threat is positively related to distress, but mastery mediates this relationship nonlinearly, with the indirect relationship between threat and distress strengthening as threat increases. The moderating function of mastery is also nonlinear, with moderate levels of mastery providing maximum stress buffering. This research suggests that contextual conditions of constraint can create nonlinearities in the way that mastery mediates and moderates the effects of ambient stressors.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.066
GPT teacher head0.401
Teacher spread0.334 · 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 source (direct Gemma or distilled Codex), 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

Citations25
Published2014
Admission routes1
Has abstractyes

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