Recommendations to Leverage the Palliative Nursing Role During COVID-19 and Future Public Health Crises
Bibliographic record
Abstract
With the daily number of confirmed COVID-19 cases and associated deaths rising exponentially, social fabrics on a global scale are being worn by panic, uncertainty, fear, and other consequences of the health care crisis. Comprising more than half of the global health care workforce and the highest proportion of direct patient care time than any other health professional, nurses are at the forefront of this crisis. Throughout the evolving COVID-19 pandemic, palliative nurses will increasingly exercise their expertise in symptom management, ethics, communication, and end-of-life care, among other crucial skills. The literature addressing the palliative care response to COVID-19 has surged, and yet, there is a critical gap regarding the unique contributions of palliative nurses and their essential role in mitigating the sequelae of this crisis. Thus, the primary aim herein is to provide recommendations for palliative nurses and other health care stakeholders to ensure their optimal value is realized and to promote their well-being and resilience during COVID-19 and, by extension, in anticipation of future public health 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 | 0.010 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.029 | 0.009 |
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".