Exploring the factors affecting ICU nurse retention during and post-COVID-19: A qualitative descriptive interview study
Bibliographic record
Abstract
OBJECTIVES: ICUs have been shown to experience high staff turnover rates, exacerbated by the COVID-19 pandemic. Shortages in nurse staffing have been linked to worse patient outcomes. The purpose of this study was to determine the factors most likely to promote nurse retention in the context of the COVID-19 pandemic. METHODS: This was a qualitative interview study in which 19 registered nurses in a single ICU were asked which factors contribute to ICU nurse turnover and attrition, as well as improve staff retention. We recruited participants who had either left or considered leaving their ICU position since the onset of the COVID-19 pandemic. We used Braun and Clarke's method of thematic analysis to generate themes from the interviews, which were video, or audio recorded. Using NVivo software, data were coded by assigning concepts to data segments. These codes were consolidated into categories and further combined to create themes. The study followed the standards outlined in the COREQ checklist. RESULTS: We generated four themes to capture the aspects that most influenced participants' desire to remain in their ICU positions. Themes included Organizational Resources and Scheduling, Interpersonal Factors, Mental Health Support and Training and Career Advancement. CONCLUSIONS: This study explored the key factors that impact nurses' willingness to continue working in the ICU following the COVID-19 pandemic. Participants highlighted how administrative change, workplace relationships, access to mental health services, and availability of professional development opportunities may have positively influenced their decision to stay. The findings described may prove valuable avenues of future study as further investigation related to the themes described may help guide intervention aimed at improving ICU retention. IMPLICATIONS FOR CLINICAL PRACTICE: Together, these findings may serve to inform future ICU interventions aimed at improving nurse retention in the ICU.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".