A narrative inquiry of nursing experiences during the Covid-19 pandemic
Bibliographic record
Abstract
This research focused on nursing care during the early phase of the Covid-19 pandemic. The experiences of 18 nurses and 3 nurse practitioners were analyzed through qualitative narrative inquiry. Riessman’s analytic approach guided identification of thematic similarities. The nurses in this study were emotionally exhausted by the rapid rise in patients and the daily death toll. The challenge of so many gravely ill cases required creative adaptations to address overcrowding and lack of resources. Teamwork proved immeasurable. Nurses advocated for their patients and families and were proud to have worked in this emergency. Nurses dealt with stressors by maintaining prior coping practices and developing new ones and relied on support from their family, hospital and community. A common objective was to establish and maintain the highest-possible levels of care in spite of challenging conditions. Proficiencies developed in coping with unprecedented challenges can help plan for future healthcare crises.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| 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".