Nurses’ Experiences of their Ethical Responsibilities during Coronavirus Outbreaks: A Scoping Review
Bibliographic record
Abstract
Globally, nurses have experienced changes to the moral conditions of their work during coronavirus outbreaks. To identify the challenges and sources of support in nurses' efforts to meet their ethical responsibilities during SARS, MERS, and COVID-19 outbreaks a scoping review design was chosen. A search was conducted for eligible studies in Ovid MEDLINE, Ovid Embase and Embase Classic, EBSCO CINAHL Plus, OVID APA PsycInfo, ProQuest ASSIA, and ProQuest Sociological Abstracts on August 19, 2020 and November 9, 2020. The PRISMA-ScR checklist was used to ensure rigor. A total of 5204 records were identified of which 41 studies were included. Three themes were identified related challenges in meeting ethical responsibilities: 1) substandard care, 2) impeded relationships, 3) organizational and system responses and six themes relating to sources of support: 1) team and supervisor relationships, 2) organizational change leading to improved patient care, 3) speaking out, 4) finding meaning, 5) responses by patients and the public, 6) self-care strategies.Our review revealed how substandard care and public health measures resulted in nurses not being fully able to meet their ethical responsibilities of care. These included the visitation policies that impeded the support of patients by nurses and families, particularly with respect to face-to-face relationships. Organizational and system responses to the evolving outbreaks, such as inadequate staffing, also contributed to these challenges. Supportive relationships with colleagues and supervisors, however, were very beneficial, along with positive responses from patients and the public.
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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 | 0.031 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".