Who Should Be at the Bedside 24/7: Doctors, Families, Nurses?
Bibliographic record
Abstract
Critical illness does not keep to regular, daytime business hours; we must provide high-quality care and support for intensive care unit (ICU) patients 24 hours per day, 7 days per week. Whether this mandates the presence of similar numbers and types of personnel throughout all hours of the day, however, has been the subject of much debate and substantial research. In this article, we review the available literature on the consequences of having three groups of care providers at a patient's bedside overnight: physicians, visitors, and nurses. Though few of the studies on this topic are randomized and prospective, several themes have emerged from the existing data. First, there is dramatic variation in practice between and within countries. Second, the weight of evidence does not indicate that patient outcomes are improved by having an intensivist present overnight in ICUs that are staffed by intensivists during the daytime hours. Third, although visitation is highly restricted in many ICUs-out of concerns for disruption of care and a negative physiological or psychological impact on patients-the available data suggest that patients and their families generally benefit from open visitation policies. And finally, although there is little debate that nurses are (and should be) available in the ICU 24/7, existing data do not provide much of a consensus about the details. Uncertainties include whether outcomes are better when each nurse is assigned only one patient (or, more generally, the optimal patient:nurse ratio), who these nurses should be (e.g., registered nurses vs. other personnel), and what their roles should entail (e.g., managing ventilators). As such, we cannot yet identify the optimal overnight nurse staffing strategy. What is clear is that the critical care community needs more and better data to further define these aspects of the relationship between ICU structure and ICU outcomes.
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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".