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Record W4210433862 · doi:10.1037/tra0001216

Professional quality of life, stress, and trauma in nursing students: Before and during the novel coronavirus pandemic.

2022· article· en· W4210433862 on OpenAlexaffabout
Kathryn Chachula, Nora Ahmad

Bibliographic record

VenuePsychological Trauma Theory Research Practice and Policy · 2022
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsBrandon University
Fundersnot available
KeywordsCompassion fatigueBurnoutChecklistPsycINFOPandemicMedicinePopulationNursingMental healthQuality of life (healthcare)Scale (ratio)Clinical psychologyPsychologyFamily medicineMEDLINEPsychiatryCoronavirus disease 2019 (COVID-19)

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of the study was to assess the levels of stress, burnout, primary and secondary trauma, and self-efficacy before and during the novel coronavirus pandemic in a sample of baccalaureate nursing and psychiatric nursing students, a population which has seldom been studied regarding these factors. METHOD: The study used a nonexperimental, cross-sectional methodology at 2 time-points. Nursing and psychiatric nursing students enrolled at 1 western Canadian university were invited to participate in an online, anonymous survey in 2020 prior to the pandemic and in 2021 during Canada's third-wave. Survey measures included the Professional Quality of Life Scale (includes Compassion Satisfaction, Burnout, and Secondary Traumatic Stress), the Perceived Stress Scale, the Life Events Checklist to assess the amount of prior traumatic experiences, and the Core Self-Evaluations Scale. RESULTS: ≤ .001). CONCLUSION: This preliminary study is the first to reveal that students in the nursing field experienced more traumatic events during the pandemic than before. The findings imply that access to greater support for experiences of trauma may be needed to support undergraduate students entering the health care arena amid the novel coronavirus pandemic. (PsycInfo Database Record (c) 2022 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)0.009
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 score0.558
Threshold uncertainty score0.720

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.371
GPT teacher head0.641
Teacher spread0.270 · 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

Citations16
Published2022
Admission routes2
Has abstractyes

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