Impact of the COVID-19 Pandemic on Nurses Working in Intensive Care Units: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic resulted in significant system strain, requiring rapid redeployment of nurses to intensive care units. Little is known about the impact of the COVID-19 pandemic and surge models on nurses. OBJECTIVE: To identify the impact of the COVID-19 pandemic on nurses working in intensive care units. METHODS: A scoping review was performed. Articles were excluded if they concerned nurses who were not caring for critically ill adult patients with COVID-19, did not describe impact on nurses, or solely examined workload or expansion of pediatric intensive care units. RESULTS: This search identified 417 unique records, of which 55 met inclusion criteria (37 peer-reviewed and 18 grey literature sources). Within the peer-reviewed literature, 42.7% of participants were identified as intensive care unit nurses, 0.65% as redeployed nurses, and 72.4% as women. The predominant finding was the prevalence of negative psychological impacts on nurses, including stress, distress, anxiety, depression, fear, posttraumatic stress disorder, and burnout. Women and members of ethnic minority groups were at higher risk of experiencing negative consequences. Common qualitative themes included the presence of novel changes, negative impacts, and mitigators of harm during the pandemic. CONCLUSIONS: Nurses working in intensive care units during the COVID-19 pandemic experienced adverse psychological outcomes, with unique stressors and challenges observed among both permanent intensive care unit and redeployed nurses. Further research is required to understand the impact of these outcomes over the full duration of the pandemic, among at-risk groups, and within the context of redeployment roles.
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 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 | 0.008 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".