Navigating the Storm
Bibliographic record
Abstract
Nominal research illustrates the lived experience of intensive care unit registered nurses during the COVID pandemic. Palliative care team leaders and nurse researchers designed this cross-sectional study to discover opportunities for palliative care team members to enhance the experience of nurses who cared for critically ill patients during this challenging time. The study aimed to compare the effect of caring for patients in COVID versus non-COVID units. Surveys were distributed after the area's initial COVID patient influx. Questions included general demographics, the Professional Quality of Life survey instrument (measuring compassion satisfaction, burnout, and secondary traumatic stress), and open-ended questions to identify protective factors and unique challenges. Across 5 care settings with 311 nurses eligible for the study in total, 90 completed the survey. The population consisted of COVID-designated unit nurses (n = 48, 53.33%) and non-COVID unit nurses (n = 42, 46.67%). Analysis between COVID-designated and non-COVID units revealed significantly lower mean compassion scores and significantly higher burnout and stress scores among those working within COVID-designated units. Despite higher levels of burnout and stress and lower levels of compassion, nurses identified protective factors that improved coping and described challenges they encountered. Palliative care clinicians used insights to design interventions to mitigate identified challenges and stressors.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".