Avoiding Futility: How Nurses and Physicians Experience Emotions, Psychosocial Factors, and Their Professional Roles as Influencing the End-of-Life Decision Making Process
Bibliographic record
Abstract
In this qualitative study, five nurses and four physicians from intensive care settings were interviewed about their experiences of the end-of-life (EOL) decision making process. For both professional groups, a shared mission to avoid futility was identified as foundational and climactic aim to the process. This desire heavily shaped initiation and engagement of what was presented as an ambiguous decision making process. Three themes emerged of elements that most influenced their variable experiences of the EOL decision making process: moral weightiness, family receptiveness, and the individual philosophy of approach. These findings emphasize the wide amount of subjective variability experienced and shed light on the competing emotional, psychological, and social interests for ICU nurses and physicians in the EOL decision making process. There must be greater understanding of the EOL decision making process in an intensive care context to provide better support to nurses and physicians.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".