Working on a designated COVID-19 unit: Exploring nurse perceptions and experiences
Bibliographic record
Abstract
Objective: The COVID-19 global pandemic has had a distressing effect on clinical nursing units, creating unique challenges for the nursing profession. These unprecedented challenges included constant fear of the unknown, major disruptions to daily routines, the need to adapt to the realities of new safety protocols, the need for continuous donning and doffing of PPE, and an alarming increase in patient acuity and death. Rapid increases in COVID-19 hospital admissions prompted hospital administrators to designate specific medical-surgical nursing units as covid units. As a result, nurses faced the real possibility of bringing the virus home to their loved ones and possibly contracting a deadly disease. To understand the impact of COVID-19, a study was conducted to assess nurses’ perceptions, morale, emotions, current knowledge and susceptibility to developing compassion fatigue.Methods: Content analysis was used to identify registered nurses’ perceptions and experiences while providing nursing care during the COVID-19 pandemic. Additionally, data was collected to assess participants’ emotional and physical well-being, knowledge and susceptibility to developing compassion fatigue.Results: Nurses’ qualitative responses were categorized into seven themes. Additionally, compassion fatigue knowledge and susceptibility responses were analyzed.Conclusions: Associated feedback, including narrations, provided a framework to assist nurses with accessing resources to manage stressors, combat compassion fatigue symptoms, promote resiliency, and increase communication skills.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".