Understanding and changing attitudes toward withdrawal and withholding of life support in the intensive care unit
Bibliographic record
Abstract
A careful examination of our attitudes toward end-of-life care is critical to our understanding of where change is needed to improve patient outcomes. The objectives of our narrative review are 1) to review why the intensive care unit setting presents particular challenges for the delivery of optimal end-of-life care, 2) to outline how four different research methods can provide insights into our understanding of attitudes about withdrawal of life support, and 3) to suggest seven different approaches to changing prevailing attitudes toward withdrawal of life support in the intensive care unit. To better understand attitudes about end-of-life care in general and withdrawal of life support in particular, we reviewed four different sources of data: 1) decision support tools, 2) qualitative research, 3) surveys, and 4) observational studies. Understanding these attitudes offers valuable insights about strategies that may help to improve the care of dying patients and their families. There are several ways to change attitudes; the approaches we reviewed are 1) promoting social change professionally, 2) legitimizing end-of-life research, 3) determining what families of dying patients need, 4) initiating quality improvement locally, 5) evaluating the benefits and harms of new initiatives, 6) modeling quality end-of-life care for future clinicians, and 7) using narratives. Attitudes toward end-of-life care are influenced by many factors and change slowly. Our attitudes have social and personal origins; they are grounded in values that are collective and community based. Different research methods provide insights into attitudes toward death in the intensive care unit and withdrawal of life support in particular. Understanding these attitudes may offer valuable insights about strategies that should help improve the care for dying patients and their families.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".