Exploring the Impact of <scp>COVID</scp> ‐19 on Acute Care Nurses: An Integrative Review
Bibliographic record
Abstract
AIM: To analyse, critique, and synthesise available research to create a unique framework of the impacts of COVID-19 on acute care nurses. METHODS: Whittemore and Knafl's framework was used to organise this review. The Mixed Methods Appraisal Tool was used for quality analysis. DATA SOURCES: CINAHL, MEDLINE, Web of Science, Scopus and the National Institute of Health COVID-19 database were searched. RESULTS: Twenty-five articles were included. Impacts on acute care nurses came from changes, access to resources, interrupted relationships, and the virus itself. The outcomes from nurses were categorised as positive, physical, emotional responses, leaving and mental disorders. These outcomes were mediated by making connections, coping, learning and experience, and finding meaning. CONCLUSION: Nurses working in acute care during COVID-19 were faced with immense stressors in a tumultuous and dangerous time. The vastly negative outcomes were less surprising than the fact nurses were left to find mitigating factors on their own. Given the large attrition from nursing that occurred and is still occurring, health systems that can both lessen the impacts and strengthen the buffering effects of mediating factors may fare better when the next pandemic comes. IMPLICATIONS: Lessons learned can be used to prepare for future pandemics. Nurses should be at the forefront of all planning whether through education, policy, or research. Having a framework allows for a more comprehensive understanding and provides an underpinning for future action. The possibility for impact spans nurses across the globe who have worked, and who may work, during a pandemic. This framework provides a basis for changes related to pandemic planning throughout nursing domains. REPORTING METHOD: The researcher has adhered to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement. No Patient or Public Contribution.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".