Nurses’ Experiences of Futile Care at Intensive Care Units: A Phenomenological Study
Bibliographic record
Abstract
The concept and meaning of futile care depends on the existing culture, values, religion, beliefs, medical achievements and emotional status of a country. We aimed to define the concept of futile care in the viewpoints of nurses working in intensive care units (ICUs). In this phenomenological study, the experiences of 25 nurses were explored in 11 teaching hospitals affiliated to Social Security Organization in Ghazvin province in the northwest of Iran. Personal interviews and observations were used for data collection. All interviews were recorded as well as transcribed and codes, subthemes and themes were extracted using Van Manen's analysis method. Initially, 191 codes were extracted. During data analysis and comparison, the codes were reduced to 178. Ultimately, 9 sub-themes and four themes emerged: uselessness, waste of resources, torment, and aspects of futility.Nurses defined futile care as "useless, ineffective care giving with wastage of resources and torment of both patients and nurses having nursing and medical aspects" As nurses play a key role in managing futile care, being aware of their experiences in this regard could be the initial operational step for providing useful care as well as educational programs in ICUs. Moreover, the results of this study could help nursing managers adopt supportive approaches to reduce the amount of futile care which could in turn resolve some of the complications nurses face at these wards such as burnout, ethical conflicts, and leave.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".