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Record W3091927228 · doi:10.1177/0844562120961988

Exposures to Potentially Psychologically Traumatic Events Among Nurses in Canada

2020· article· en· W3091927228 on OpenAlexafffundvenueabout
Andrea M. Stelnicki, Laleh Jamshidi, Rosemary Ricciardelli, R. Nicholas Carleton

Bibliographic record

VenueCanadian Journal of Nursing Research · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsMemorial University of NewfoundlandCanadian Institute for Public Safety Research and TreatmentUniversity of Regina
FundersCanadian Institutes of Health Research
KeywordsPsychiatryAnxietyPanic disorderMedicineMental healthOccupational safety and healthClinical psychologyInjury preventionPoison controlPsychologyMedical emergency

Abstract

fetched live from OpenAlex

BACKGROUND: Nurses are regularly exposed to diverse potentially psychologically traumatic events (PPTEs) as a function of their work. Cumulative exposure to PPTEs can lead to clinically significant symptoms of mental disorders. PURPOSE: We designed the current study to investigate the prevalence of different PPTEs among Canadian nurses and estimate the associations between diverse exposures and several mental disorders. METHODS: Canadian nurses (i.e., registered nurses, registered psychiatric nurses, licensed practical nurses, nurse practitioners) completed an online, self-reported survey. In total, 4067 participants (94.8% women) completed all relevant survey measures. RESULTS: <.05) associations between diverse traumatic events and all mental disorders (i.e., Posttraumatic Stress Disorder, Major Depressive Disorder, Generalized Anxiety Disorder, Panic Disorder) except Alcohol Use Disorder. CONCLUSIONS: The current findings suggest that Canadian nurses are substantially exposed to traumatic events, which vary by several sociodemographic categories. PPTE exposures were significantly associated with mental disorders; that is, if PPTEs were eliminated among Canadian nurses in the sample, symptoms would be reduced between 42.0% and 58.0%.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.241
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.252
GPT teacher head0.524
Teacher spread0.271 · 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

Citations32
Published2020
Admission routes4
Has abstractyes

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