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Record W2944720246 · doi:10.1080/20008198.2019.1606625

Lifetime traumatic stressors and adverse childhood experiences uniquely predict concurrent PTSD, complex PTSD, and dissociative subtype of PTSD symptoms whereas recent adult non-traumatic stressors do not: results from an online survey study

2019· article· en· W2944720246 on OpenAlexafffund
Paul Frewen, Jenney Zhu, Ruth A. Lanius

Bibliographic record

VenueEuropean journal of psychotraumatology · 2019
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsWestern University
FundersInstitute of Neurosciences, Mental Health and Addiction
KeywordsStressorDissociativeTraumatic stressClinical psychologyPsychologyPsychiatryMedicine

Abstract

fetched live from OpenAlex

This retrospective survey study compared the differential risk of lifetime traumatic stressors, so-called "non-traumatic stressors" experienced over the past year, referring to life events that do not meet the criteria for A1 traumatic events, and adverse childhood experiences (ACE) on severity of DSM-5 versus ICD-11 PTSD, Complex PTSD (CPTSD), and dissociative subtype of PTSD (D-PTSD) symptoms among 418 participants recruited online. In pairwise analyses, all stress types were associated with all outcomes. However, multiple regression and factor analyses indicated that whereas the number of different lifetime traumatic events participants reported experiencing, together with the number of ACE participants experienced, uniquely predicted DSM-5 PTSD, D-PTSD and ICD-11 PTSD and CPTSD symptoms, the number of non-traumatic stressors they experienced during the last year did not. Moreover, ACE uniquely predicted all outcomes even after accounting for lifetime traumatic stress. These results provide further support for the particularly high risk of lifetime traumatic stressors and ACE in predicting trauma and stressor-related symptoms. Future research directions are discussed.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.455
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.055
GPT teacher head0.331
Teacher spread0.275 · 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 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

Citations151
Published2019
Admission routes2
Has abstractyes

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