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Record W2625706323

PTSD, Acute Stress, Performance and Decision-Making in Emergency Service Workers.

2017· article· en· W2625706323 on OpenAlexaff
Cheryl Regehr, Vicki R. LeBlanc

Bibliographic record

VenuePubMed · 2017
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsMedical Council of Canada
Fundersnot available
KeywordsJudgementHuman factors and ergonomicsOccupational safety and healthInjury preventionCognitionMedicinePsychologyPoison controlSuicide preventionStress (linguistics)Acute Stress DisorderMedical emergencyClinical psychologyPsychiatryAnxiety
DOInot available

Abstract

fetched live from OpenAlex

Despite research identifying high levels of stress and traumatic stress symptoms among those in the emergency services, the impact of these symptoms on performance and hence public safety remains uncertain. This review paper discusses a program of research that has examined the effects of prior critical incident exposure, acute stress, and current post-traumatic symptoms on the performance and decision-making during an acutely stressful event among police officers, police communicators, paramedics and child protection workers. Four studies, using simulation methods involving video simulators, human-patient simulators, and/or standardized patients, examined the performance of emergency workers in typical workplace situations related to their individual profession. Results varied according to level of acuity of stress and the nature of performance and decision-making. There was no evidence that PTSD had a direct impact on global performance on tasks for which emergency responders are highly trained. However, PTSD was associated with assessment of risk in situations that required professional judgement. Further, individuals experiencing PTSD symptoms reported higher levels of acute stress when faced with high acuity situations. Acute stress in these studies was associated with performance deficits on complex cognitive tasks, verbal memory impairment and heightened assessment of risk.

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)0.000
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 score0.452
Threshold uncertainty score0.591

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.070
GPT teacher head0.375
Teacher spread0.305 · 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

Citations86
Published2017
Admission routes1
Has abstractyes

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