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Record W4398766821 · doi:10.1155/2024/5579322

Moral Distress, Burnout, Turnover Intention, and Coping Strategies among Korean Nurses during the Late Stage of the COVID-19 Pandemic: A Mixed-Method Study

2024· article· en· W4398766821 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Nursing Management · 2024
Typearticle
Languageen
FieldNursing
TopicHealthcare Education and Workforce Issues
Canadian institutionsnot available
FundersCollege of Nursing, Yonsei UniversityYonsei UniversityMo-Im Kim Nursing Research Institute, Yonsei University College of MedicineYork UniversityNew York University
KeywordsBurnoutTurnover intentionCoronavirus disease 2019 (COVID-19)PandemicDistressPsychologyCoping (psychology)2019-20 coronavirus outbreakClinical psychologySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)MedicineNursingJob satisfactionSocial psychologyInternal medicineDiseaseVirology

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has exacerbated the difficulties nurses face, resulting in higher turnover rates and workforce shortages. This study investigated the relationships between nurses' moral distress, burnout, and turnover intention during the last stage of the COVID-19 pandemic. It also explored the coping strategies nurses use to mitigate moral distress. Utilizing a mixed-method approach, this study analyzed data from 307 nurses caring for patients with COVID-19 in acute care hospitals through an online survey conducted in November 2022. Our data analysis encompassed quantitative methods, including descriptive statistics and path analysis, using a generalized structural equation model. For the qualitative aspect, we examined open-ended responses from 246 nurses using inductive content analysis. The quantitative findings revealed that nurses' moral distress had a significant direct effect on turnover intention. In addition, burnout significantly mediated the relationship between moral distress and turnover intention. Qualitative analyses contextualized the relationships uncovered in the quantitative analyses. The qualitative analysis identified various positive and negative coping strategies. Positive strategies included a commitment to minimize COVID-19 transmission risks, adopting a holistic approach amidst the challenges posed by the pandemic, voicing concerns for patient safety, engaging in continuous learning, and prioritizing self-care. Conversely, negative strategies involved adopting avoidance behaviors stemming from feelings of powerlessness and adopting a passive approach to one's role. Notably, some participants shifted from positive to negative coping strategies because of institutional barriers and challenges. The findings underscore the importance for hospital administrators and nurse managers to acknowledge the impact of the pandemic-related challenges encountered by nurses and recognize the link among moral distress, burnout, and turnover intention. It highlights the essential role of organizational and managerial support in fostering effective coping strategies among nurses to address moral distress.

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.

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)0.001
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 score0.124
Threshold uncertainty score0.431

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
Research integrity0.0000.000
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 head0.050
GPT teacher head0.403
Teacher spread0.353 · 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