Ethical challenges faced by nurses during the COVID-19 pandemic: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The aim of this scoping review is to describe the literature reporting on ethical challenges faced by nurses during the COVID-19 pandemic, including the contextual characteristics of ethical challenges, and the strategies to address these challenges. INTRODUCTION: The COVID-19 pandemic presented many ethical challenges to nurses, ranging from allocating scarce resources, to balancing a duty of care with self-preservation, and implementing visitation restrictions. Internationally, there has been a range of reported issues, but few studies have described strategies to overcome these challenges. INCLUSION CRITERIA: Studies that report on ethical challenges faced by nurses while caring for patients during the COVID-19 pandemic will be included. Studies that report on strategies to address these challenges will also be considered for inclusion. METHODS: This scoping review will be conducted in accordance with the methods outlined by JBI and reported using PRISMA-ScR guidance. The following databases will be searched for eligible studies from November 2019 to present day: PubMed, CINAHL, Ovid, PsycINFO, the Cochrane Library, and Scopus. No language restrictions will be applied. Studies will be reviewed for inclusion by 2 independent reviewers and a data extraction form developed specifically for this review will be used to extract data relevant to the review questions. Results will be analyzed and presented according to the concepts of interest, using tables, figures, images, and supporting narrative synthesis.
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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 | 0.145 | 0.106 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.020 | 0.013 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.042 | 0.012 |
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".