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Record W4413375274 · doi:10.1037/tra0002000

Screening positively for PTSD: Examining the role of avoidance for public safety personnel.

2025· article· en· W4413375274 on OpenAlexaffabout
Robyn E. Shields, Terence M. Keane, Blake A. E. Boehme, Gordon J. G. Asmundson, R. Nicholas Carleton

Bibliographic record

VenuePsychological Trauma Theory Research Practice and Policy · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsPsycINFOPsychologyClinical psychologyMEDLINEPolitical science

Abstract

fetched live from OpenAlex

OBJECTIVE: = 2,631). METHOD: Prevalence of positive PTSD screens was calculated for PSP sectors, the total PSP sample, and the general population. Differences in PTSD screening prevalences based on removing each of the four symptom cluster requirements were compared among and between PSP groups and the general population. RESULTS: s < .05), but not municipal/provincial police and public safety communicators. CONCLUSIONS: s < .01), suggesting PSP may be strictly limited by avoidance items due to service requirements. Results indicated ∼2% (∼100,000 PSP across Canada, the United States, and Australia) may be currently experiencing symptoms of PTSD, but not screening positively; thus PSP may benefit from PCL-5 revisions to better identify their avoidance behaviors. (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)0.013
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.899
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.231
GPT teacher head0.515
Teacher spread0.284 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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