The Impact of COVID-19 on Canadian Acute Care Nursing Professionals: An Integrative Review
Bibliographic record
Abstract
Background: The COVID-19 pandemic has put widespread pressures on the Canadian healthcare system. As infections soared and the healthcare system attempted to grapple with increased patient loads and acuity, nurses were impacted and, among other issues, began leaving the profession. Shortfalls of acute care staff have caused bed closures, service disruptions, and decreased access to timely patient care. In order to stem the tide of nurses exiting the profession, the system needs to change. Understanding the complexity of pandemic impacts on nurses is integral to making changes that will be most impactful as COVID-19 continues and as new pandemics occur. Purpose: To integrate the current evidence of COVID-19 impacts on Canadian acute care nurses. Method: This is an integrative review using Whittemore and Knafl’s framework. The databases searched were CINAHL, MEDLINE, Web of Science, and NIH. The search terms used were Canada/Canadian/Canadians, the name of each province, nurse/nurses/nursing, and COVID 19/SARS-CoV-2/coronavirus/cov-19. Search criteria were supported by a health sciences librarian. Twenty articles were found to meet inclusion criteria and contribute to an understanding of the impacts of the COVID-19 pandemic on acute care nurses specifically. Results: The current evidence has been synthesized into a conceptual framework which indicates the COVID-19 pandemic impacted acute care nurses in Canada through four main mechanisms: a) changes which sometimes occurred rapidly and frequently; b) access to needed resources; c) connections between nurses as well as others; and d) aspects of the infectious agent itself. The outcomes of the pandemic on nurses included positive effects, physical effects, emotional responses, leaving/attrition, and mental health disorders. As well, four significant mediating factors were identified as coping, making connections, learning and experience, and finding meaning. Application to Nursing: Nurses have been impacted by the COVID-19 pandemic in ways that are far-reaching and possibly long-term. Understanding how these impacts occur, can help in the formation of policies and procedures that can mitigate the effects and support nurses even during pandemic events.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.022 | 0.027 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".