Thinking about strengths in end-of-life nursing practice: the case of intensive care unit nurses
Bibliographic record
Abstract
BACKGROUND: The intensive care unit (ICU) is a care context that is sometimes described as being unconducive to the values and ideals of a good death in end-of-life care. Such assumptions render the ICU emblematic of a troubling discourse about end-of-life care in this clinical context. AIM: To stimulate a reflective examination of intensive care nursing practice with respect to end-of-life care. METHODS: The work of contemporary nursing scholar Laurie Gottlieb is used to perform a strengths-based relational ethical examination of previously published literature that describes critical care nurses' experiences of providing end-of-life care in the ICU. FINDINGS: This literature suggests that the relational ethical value of authentic engagement, which is fundamental to the disciplinary ethos of expert palliative care nursing, is reflected in the everyday practice of intensive care nurses whose patients die while under their care. CONCLUSION: A strengths-based approach can make visible the relational ethical practice of critical care nurses who care for dying patients and their families in the ICU.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.039 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.030 | 0.075 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 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".