The Role of Uncertainty in the Experiences of Nurses During the Covid-19 Pandemic: A Phenomenological Study
Bibliographic record
Abstract
BACKGROUND: The novel coronavirus (Covid-19) has spread quickly to all corners of the globe and caused high rates of morbidity and mortality. Nurses have been at the centre of this experience managing the outbreak through direct bedside care, managing hospital units, providing Covid-19 testing, and contact tracing. PURPOSE: The aim of this article is to examine the role that nurses played in the early stages of the Covid-19 pandemic through the voices of the participants. METHODS: Using a phenomenological methodology thirty-one interviews were completed via phone and thematic analysis was completed. RESULTS: The major themes that emerged from this phenomenological study were: emotional challenges, uncertainty, and protective factors. Emotional challenges included, stress, anxiety, exhaustion, frustration, guilt, and loneliness. These challenges were magnified by uncertainty through leadership and communication challenges, needs of the pandemic versus needs of the patient, and Covid-19 and best practice. In this study, emotional challenges were mitigated by the protective factors of: education, ability to contribute, team cohesiveness, and community support. CONCLUSIONS: Nurses are challenged during this time but by limiting uncertainty and providing protective factors, nurses can be less affected by emotional challenges and able to provide nursing care and manage the outbreak effectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".