“It never ends…”-A qualitative study of nurses’ experience of caring for hospitalised unvaccinated patients with COVID-19 in Sweden
Bibliographic record
Abstract
Introduction: COVID-19 was considered a pandemic as of mid-March 2020 until May 2023. The first vaccine against COVID-19 gained approval at the end of 2020. Overall willingness to be vaccinated is high in Sweden, some people have refused the vaccine. The pandemic caused trauma to nurses around the world due to heavy workloads, deaths in the profession, and employers’ failure to prioritise nurses’ physical and mental well-being. This, together with hesitation to get vaccinated, might have affected nurses’ work. Therefore, it is important to investigate how nurses were affected during the COVID-19 pandemic and their work with unvaccinated patients. The aim was to explore nurses’ experience of caring for hospitalised unvaccinated patients with COVID-19.Methods: A qualitative approach was used to describe nurses’ perceptions and experiences. Nine semi-structured interviews were conducted in the spring of 2022. The study was set in two departments of infectious care at tertiary care emergency hospitals in Stockholm, Sweden. Results: The findings are presented with four themes: A difficult work situation; The strength of colleagues; Dealing with different opinions; and Lessons learned from the pandemic. Each theme has two subthemes.Conclusions: The nurses were often working under stress during the pandemic, and they showed signs of compassion fatigue, which affected the nurses and, by extension, their unvaccinated patients. For pandemics, epidemics and challenges to come, our findings show that there is a need for mandatory reflection and scenario-based training to increase resilience and competence and to prevent compassion fatigue.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".