Antecedents of burnout and turnover intentions during the COVID-19 pandemic in critical care nurses: A mediation study
Bibliographic record
Abstract
Background: Nurses working in critical care environments have experienced a great deal of psychological stress during the successive waves of the COVID-19 pandemic. Identifying factors which contribute to burnout and turnover intentions are important to retain intensive care unit (ICU) nurses. Purpose: The purpose of this study is to identify factors that are directly and indirectly associated with burnout and turnover intentions in ICU nurses. Methods: A cross-sectional design was used with survey data during the peak of the second wave of the COVID-19 pandemic. Data were collected through an online survey and analyzed using mediation analysis. A total of 236 ICU nurses across Canada participated in the study. Results: The results indicate that burnout mediates the relationship between moral distress, organizational support, resilience, and turnover intentions. Moreover, 49% of the participants were considering leaving. The reasons were related to lack of administrative support, poor work environment and safety concerns. Discussion: Organizational support and individual resilience can both play a role in turnover intentions through the prevention of burnout symptoms. Managers at all levels play an important role in mitigating the harmful effects of the pandemic. Conclusion: The pandemic has had a serious psychological impact on ICU nurses. Targeted interventions are needed to support this group.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".