A Journey of Uncertainty: Learned Lessons From the Lived Experiences of Nurses in Kuwait Taking Care of COVID-19 Patients in the Early Pandemic
Bibliographic record
Abstract
Purpose of the Study: This study explored the experiences of nurses in Kuwait who worked with COVID-19 patients during the first wave of the disease. Study Design: This was a qualitative descriptive study. Methods Used: In-depth interviews were conducted with seven (7) nurses who worked in intensive care units between September 2020 and March 2021. Findings: The experiences of Kuwait nurses in COVID-19 care showed an evolving journey of dealing with a strange and complex disease. With little known about the disease, the nurses approached COVID-19 care with uncertainty and ambivalence, unsure of where this journey would look like. Four themes emerged from the data and they included (1) from challenges to coping, (2) focusing on good health throughout the pandemic, (3) navigating through scarce resources and power dynamics, and (4) a multi-dimensional burden. Conclusions: Despite the difficulties encountered, supportive systems such as the availability of medical supplies, and support from superiors, colleagues, the community, and families, helped Kuwait nurses to cope with the stresses of an early COVID-19 pandemic while providing care. This approach takes a holistic stance to care for both patients and the nurses working in an epidemic setting.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.026 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.003 | 0.007 |
| 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".