The Complex Dynamics of Decision-Making at the End of Life in the Intensive Care Unit: A Systematic Review of Stakeholders' Views and Influential Factors
Bibliographic record
Abstract
A lack of consensus resulting in severe conflicts is often observed between the stakeholders regarding their respective roles in end-of-life (EOL) decision-making in the ICU. Since the burden of these decisions lies upon the individuals, their opinions must be known by medical, judicial, legislative, and governmental authorities. Part of the solution to the issues that arise would be to examine and understand the views of the people in different societies. Hence, in this systematic review, we assessed the attitudes of the physicians, nurses, families, and the general public toward who should be involved in decision-making and influencing factors. Toward this, we searched three electronic databases, i.e., PubMed, CINAHL (Cumulative Index to Nursing & Allied Health), and Embase. A matrix was developed, discussed, accepted, and used for data extraction by two independent investigators. Study quality was evaluated using the Newcastle-Ottawa Scale. Data were extracted by one researcher and double-checked by a second one, and any discrepancies were discussed with a third researcher. The data were analyzed descriptively and synthesized according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Thirty-three studies met our inclusion criteria. Most involved healthcare professionals and reported geographic variations in different timeframes. While paternalistic features have been observed, physicians overall showed an inclination toward collaborative decision-making. Correspondingly, the nursing staff, families, and the public are aligned toward patient and relatives' participation, with nurses expressing their own involvement as well. Six categories of influencing factors were identified, with high-impact factors, including demographics, fear of litigation, and regulation-related ones. Findings delineate three key points. Firstly, overall stakeholders' perspectives toward EOL decision-making in the ICU seem to be leaning toward a more collaborative decision-making direction. Secondly, to reduce conflicts and reach a consensus, multifaceted efforts are needed by both healthcare professionals and governmental/regulatory authorities. Finally, due to the multifactorial complexity of the subject, directly related to demographic and regulatory factors, these efforts should be more extensively sought at a regional level.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".