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Record W4409772950 · doi:10.1080/16506073.2025.2491467

A brief workplace intervention for anxiety sensitivity aimed at reducing the risk of posttraumatic stress in first responders

2025· article· en· W4409772950 on OpenAlexaffabout
Breagh C. Newcombe, Janine V. Olthuis, Emma R. Giberson

Bibliographic record

VenueCognitive Behaviour Therapy · 2025
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsAnxiety sensitivityPosttraumatic stressAnxietyPsychologyIntervention (counseling)Clinical psychologyStress (linguistics)PsychotherapistMedicinePsychiatry

Abstract

fetched live from OpenAlex

First responders are repeatedly exposed to trauma in the course of their work, increasing their vulnerability to posttraumatic stress disorder (PTSD). Efforts to reduce the risk of PTSD could focus on individual factors that increase the risk for PTSD. Although many of these factors are immutable, others, such as high anxiety sensitivity (AS), can theoretically be targeted and fortified through intervention. The current study investigated the effectiveness of a brief (single session) cognitive behavioural therapy for AS intervention vs. a waitlist control in reducing AS, and subsequently mitigating PTSD and related mental health symptoms. Participants were 179 first responders from eight workplaces in Canada. The intervention was delivered remotely to these workplaces in a group format. Workplaces were randomly assigned to the intervention or waitlist control. Participants completed a self-report questionnaire pre-intervention, one-week post-intervention, and 8 months later (and at comparable time points in the control condition). Hierarchical linear regressions revealed that intervention conditions did not predict (a) AS during one-week follow-up or (b) PTSD and related symptoms during eight-month follow-up. Theoretical and clinical implications, including recommendations specific to interventions involving first responders, are discussed in detail.

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)0.001
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 score0.136
Threshold uncertainty score0.855

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.055
GPT teacher head0.387
Teacher spread0.332 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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