Providing End of Life Care in the Intensive Care Unit
Bibliographic record
Abstract
Critical Care Nurses have an important and integral contribution to make in the provision and enhancement of end-of-life (EOL) care through their varied roles. Due to the fact that end-of-life care is emerging as a comprehensive area of expertise in ICU, these contributions can be provided through direct practice, research, education, administration and policy. EOL care demands the same level of knowledge and competence as all other areas of ICU practice (Truog). CACCN endorses the Canadian Nurses Association (CNA) position statement on “Providing Nursing Care at the End of Life”. End-of-life care is rooted in the CNA’s Code of Ethics for Registered Nurses. The code endorses that nurses strive to foster comfort, alleviate suffering, provide adequate pain and symptom relief, and support a dignified and peaceful death. CACCN endorses that the following factors are essential for nursing practice for patients who must spend their final days in the ICU environment: Every patient and their significant others as defined by the patient have a right to information about prognosis and the benefit of interventions. The term “benefit ” may range along a continuum from significant, uncertain to no benefit. The provision of knowledge regarding prognosis and benefit of interventions allows the patient and family to make informed decisions about the suitable course of action, including when applicable, the
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".