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Record W4322491004 · doi:10.1016/j.paid.2023.112141

The roles of personality and resilience in associations between combat experiences and posttraumatic stress disorder among Canadian Armed Forces Veterans

2023· article· en· W4322491004 on OpenAlexaffabout
Rachel A. Plouffe, Anthony Nazarov, Callista Forchuk, Julia Gervasio, Tri Le, Jenny J. W. Liu, Maede S. Nouri, Cassidy Trahair, Deanna L. Walker, J. Don Richardson

Bibliographic record

VenuePersonality and Individual Differences · 2023
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsSt Joseph's Health CareToronto Metropolitan UniversityParkwood InstituteMcMaster UniversityLawson Health Research InstituteWestern University
FundersU.S. Department of Veterans Affairs
KeywordsPsychologyExtraversion and introversionPersonalityPsychological resilienceBig Five personality traitsAgreeablenessPosttraumatic stressClinical psychologyOpenness to experienceMental healthPsychiatryMilitary personnelService memberMilitary serviceSocial psychology

Abstract

fetched live from OpenAlex

Canadian Armed Forces (CAF) Veterans encounter unique challenges associated with their service. Exposure to service-related traumatic events places them at risk for developing adverse mental health outcomes, including posttraumatic stress disorder (PTSD). Our research aimed to assess whether the HEXACO personality model and resilience impacted associations between combat experiences and PTSD symptomatology in CAF Veterans in the past month. We recruited a sample of 245 CAF Veterans (81 % men; Mage = 48.47, SDage = 10.34) to complete a battery of questionnaires. PTSD symptoms were significantly associated with more combat experience, lower resilience, lower extraversion, higher emotionality, and lower agreeableness. However, personality traits did not moderate the relationship between combat experiences and PTSD symptoms. Overall, this research can be used to enhance researchers' and clinicians' understanding of personality traits as risk and protective factors for PTSD symptoms.

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)0.001
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 score0.343
Threshold uncertainty score0.792

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
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 head0.107
GPT teacher head0.362
Teacher spread0.256 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

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