Nursing care for patients at end of life in the adult intensive care unit
Bibliographic record
Abstract
The care patients receive at the end of life in the intensive care unit (ICU) is highly dependent on the ICU nurse’s knowledge, skill, and comfort level in caring for the dying patient and the patient’s family. However, formal nursing education supports the acute care culture with little or no curriculum offered on end-of-life care. A search for national standards and best practices and participating in the July 2010 End-of-Life Nursing Education Consortium (ELNEC) Train the Trainer conference led to a needs assessment on how best to educate nurses on quality end of life care in the ICU setting. By identifying the nursing education and skills needed for quality end-of-life care in the ICU on the basis of best practices and national standards, ICU nurses can be empowered to provide optimal end-of-life care. Staff education and development is a key strategy for implementing evidenced-based end-of-life care in the ICU setting. Investing in ELNEC training, using available tools such as the IPAL-ICU screening tool to identify unmet palliative needs, training end-of-life resource nurses for specific hospital units, and offering education for hospital staff can begin to raise awareness regarding end-of-life care and change the existing culture. Developing an evidenced-based order set to treat symptoms of the dying patient can help to ensure that such patients’ symptoms are well managed. A willingness to take information about the end of life to the community, such as a partnership with a local nursing school, is a key strategy for filling the gaps in knowledge in end of life care.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".