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Record W3157614953 · doi:10.1111/jnu.12668

How Emergency Nurses Develop Resilience in the Context of Workplace Violence: A Grounded Theory Study

2021· article· en· W3157614953 on OpenAlexaff
Chin‐Yen Han, Li‐Chin Chen, Chun‐Chih Lin, Suzanne Goopy, Hui‐Ling Lee

Bibliographic record

VenueJournal of Nursing Scholarship · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsUniversity of Calgary
FundersChang Gung Memorial Hospital, LinkouChang Gung Medical Foundation
KeywordsGrounded theoryWorkplace violenceContext (archaeology)Psychological resilienceNursingResilience (materials science)Emergency nursingPsychologyHealth careSuicide preventionPoison controlMedicineEmergency departmentQualitative researchMedical emergencySocial psychologySociologyPolitical science

Abstract

fetched live from OpenAlex

PURPOSE: To understand how emergency nurses develop resilience in the context of workplace violence. DESIGN: This study employed grounded theory methodology. Thirty nurses from three hospital emergency departments in Taiwan were interviewed between August and December 2018. METHODS: Semistructured interviews were used to collect data. Interviews were audio-recorded and transcribed verbatim. FINDINGS: The process through which emergency nurses who had experienced workplace violence developed resilience took place in three stages: the release of emotions after the assault; the interpretation of conflicting thoughts and actions; and the establishment of strategies to cope with workplace violence in the future. The core theme was the motivating role of professional commitment to emergency patient care. CONCLUSIONS: The results of this study can inform the development of support systems to enhance the resilience of nurses experiencing workplace violence by alerting healthcare administrators and governing institutions to their needs. CLINICAL RELEVANCE: Emergency nurses viewed professional growth and professional commitment as an invisible motivator in the development of resilience following an encounter with workplace violence.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.548
Threshold uncertainty score0.575

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.375
Teacher spread0.320 · 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 designQualitative
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
Published2021
Admission routes1
Has abstractyes

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