Factors Associated with Working During the COVID-19 Pandemic and Intent to Stay at Current Nursing Position
Bibliographic record
Abstract
The pandemic exacerbated job stress and burnout among nurses, increasing turnover and intentions to leave, in a workforce struggling with severe shortages. Shortages and turnover are associated with decreased quality of care, poor nurse health, and increased costs. This article reports the findings of a study that sought to identify characteristics of the job, work environment, and psychosocial health outcomes that may predict nurses' intent to stay at their current nursing position within the next year. Utilizing a cross-sectional design, we electronically surveyed working nurses (n = 629) during the summer of 2020 across 36 states. Demographics, work characteristics, and validated measures of anxiety, insomnia, and depressive symptoms were assessed. Logistic regression models identified factors associated with nurses' intent to stay at their jobs. Colleague support, organizational support, and organizational pandemic preparedness were associated with increased odds of intent to stay, while both mild and moderate/severe depressive symptoms were associated with decreased odds of intent to stay. Because over a quarter of nurses surveyed reported moderate to severe depressive symptoms, which were strongly associated with turnover intention, organizational leadership should examine mental health resources available to nurses and work characteristics that could be contributing to nurses' poor psychosocial health. Additionally, further research is needed to assess the meaning of organizational support to nurses in a post-COVID-19 context, as well how to create a work environment in which nurses are able to provide support to their colleagues.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".