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Record W2800112276 · doi:10.5206/uwomj.v86i2.2021

Vicarious trauma and secondary traumatic stress in health care professionals

2017· article· en· W2800112276 on OpenAlexvenueno aff
Nicole A. Guitar, Monica L. Molinaro

Bibliographic record

VenueUniversity of Western Ontario Medical Journal · 2017
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsnot available
Fundersnot available
KeywordsCompassion fatigueTraumatic stressMental healthBurnoutMedicineHealth carePsychological traumaPsychiatryPsychologyClinical psychology

Abstract

fetched live from OpenAlex

Three-quarters of Canadians are exposed to a traumatic event sufficient to cause psychological trauma in their lifetime. In fact, post-traumatic stress disorder is a global health issue with a prevalence as high as 37%. Health care professionals trained to provide mental health treatment for these individuals are at risk of developing vicarious trauma and secondary traumatic stress, both of which result in adverse symptoms for the health care provider that often mimic post-traumatic stress disorder (PTSD). Vicarious trauma develops over time as the clinician is continually exposed to their clients’ traumatic experiences, while clinicians experiencing secondary traumatic stress begin to experience the symptoms of PTSD due to secondary exposure of the traumatic event. Both vicarious trauma and secondary traumatic stress cause mental, physical, and emotional issues for health care professionals that include burnout and decreased self-worth. Health care systems and administration should aim to develop training and professional education for health care providers. This review will emphasize what factors lead to the development of vicarious trauma and secondary traumatic stress, and what aids or supports can be implemented to treat the symptoms. The implications for policy development and training will be 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 categoriesScience and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.645
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.043
GPT teacher head0.383
Teacher spread0.340 · 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

Citations21
Published2017
Admission routes1
Has abstractyes

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