Ethical challenges nurses faced during the COVID-19 pandemic: Scoping review
Bibliographic record
Abstract
Nurses encountered a myriad of ethical challenges during the height of the COVID-19 pandemic, such as allocation of scarce resources, the need to balance duty of care with safety of self as well as visitation restrictions. The impact of these challenges on the nursing workforce requires investigation. The aim of this review was to scope and describe the reported literature on ethical challenges faced by nurses during the COVID-19 pandemic, including contextual characteristics and strategies reported to address these challenges. The review was conducted in accordance with JBI methods for scoping reviews and reported using PRISMA-ScR guidance. A published protocol guided conduct of the review. The following databases were searched for eligible studies from November 2019 to January 2023: PubMed, CINAHL, Ovid, PsycINFO, the Cochrane Library, and Scopus. No language restrictions were applied. Studies were reviewed for inclusion by two independent reviewers, and a data extraction form was developed to extract data relevant to the review questions. Results were analyzed and presented according to the concepts of interest, using tables, figures, and supporting narrative synthesis. After searching the databases, 2150 citations were retrieved with 47 studies included in the review. Studies represented 23 countries across five continents. Most of the studies used qualitative designs. Ethical challenges were described in several ways, often without appealing to common ethics language or terms. Few studies reported on strategies to address the specific challenges, which may reflect the dynamic nature of the pandemic. The scoping review highlights the complex and, at times, overwhelming impact of ethical challenges faced by nurses across the globe during the COVID-19 pandemic. Findings from the review can be used as a basis for further research to explore, develop, and implement strategies to address ethical challenges faced by nurses during future public health crises.
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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 | 0.033 | 0.161 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| 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".