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Record W4388281005 · doi:10.1037/tra0001601

Factor structure and factorial invariance of the PTSD checklist for DSM-5 in public safety personnel: Results from a large and diverse sample.

2023· article· en· W4388281005 on OpenAlexaff
Blake A. E. Boehme, Robyn E. Shields, R. Nicholas Carleton, Gordon J. G. Asmundson

Bibliographic record

VenuePsychological Trauma Theory Research Practice and Policy · 2023
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsPsychologyAnhedoniaMeasurement invarianceClinical psychologyConfirmatory factor analysisChecklistDysphoriaFactorialMoodArousalStructural equation modelingPsychiatrySocial psychologyStatisticsAnxiety

Abstract

fetched live from OpenAlex

OBJECTIVE: = 5,855) and diverse PSP sample. METHOD: = 98) were conducted using six competing factor models of the PCL-5 across seven PSP sectors, five age groups, and two gender groups. RESULTS: The seven-factor hybrid model of PTSD (i.e., reexperiencing, avoidance, negative alterations in cognitions and mood, hyperarousal, intrusion, emotional numbing, dysphoria, dysphoric arousal, anxious arousal, anhedonia, negative affect) produced consistently superior fit across all sectors assessed and produced marginally better absolute values than the six-factor anhedonia model, supporting PCL-5 factorial invariance among PSP. CONCLUSIONS: The current study is the first to use a large and diverse PSP sample to assess PCL-5 factorial invariance. The results support the PCL-5 as invariant across PSP sectors, age groups, and men and women. Consistent with other studies, the seven-factor hybrid model of PTSD produced the best fit, followed closely by the six-factor anhedonia model. Future research could use structured clinical interviews to further investigate the factorial structure and invariance of PTSD symptoms among PSPs. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

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)0.005
metaresearch head score (Gemma)0.036
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.685
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.036
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.324
GPT teacher head0.511
Teacher spread0.187 · 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 designTheoretical or conceptual
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

Citations14
Published2023
Admission routes1
Has abstractyes

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