Causes of futile care from the perspective of intensive care unit nurses (I.C.U): qualitative content analysis
Bibliographic record
Abstract
BACKGROUND: Medical care that has therapeutic effects without significant benefits for the patient is called futile care. Intensive Care Units are the most important units in which nurses provide futile care. This study aimed to explain the causes of futile care from the perspective of nurses working in Intensive Care Units are. METHOD: The study was conducted using a qualitative approach. Qualitative content analysis was used to analyze the data. Study participants were 17 nurses who were working in the Intensive Care Units are of hospitals in the north of Iran. They were recruited through a purposeful sampling method. Data was gathered using in-depth, semi-structured interviews from March to June 2021. Recruitment was continued until data saturation was reached. RESULTS: Two main themes, four categories, and thirteen subcategories emerged from the data analysis. The main themes were principlism and caring swamp. The categories were moral foundation, professionalism, compulsory care, and patient's characteristics. CONCLUSION: In general, futile care has challenged nursing staff with complex conflicts. By identifying some of these conflicts, nurses will be able to control such situations and plan for better management strategies. Also, using the findings of this study, nursing managers can adopt supportive strategies to reduce the amount of futile care and thus solve the specific problems of nurses in intensive care units such as burnout, moral stress, and intention to leave.
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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".