A Comprehensive Review on the Synergy between Emergency Services, Nurses, Assistant Nurses, and Laboratory Teams in Critical Care
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Emergency services as well as the nurses, assistant nurses and the laboratory teams, must work hand in hand when providing critical care. In critically-acclaimed cases where time is of the essence, interdisciplinary coordination enhances the diagnosis, delivery of treatments and patient outcomes. The emergency patient is kept safe, rapidly moved, and treated by triage and early care, followed by ward nurses and assistant nurses who can perform continuous observations, supported by laboratory personnel who provide essential information needed for treatment. However, collaboration is often faced with barriers such as communication breakdown, organizational structure, and lack of standard use of technology. Works released between 2010 and 2020 indicate that standardization of information transfer, such as the SBAR model, and embracing clinical information technology, such as EHR, improves team coordination, minimizes adverse events, and shortens reaction time. Also, interdisciplinary training is another important practice that helps ensure that different departments have enough trust for one another, enabling better integration. Since the changes in attitudes towards interdisciplinary collaboration, new technologies such as data sharing and diagnostics have enhanced the flow of information between teams. However, the patchy implementation throughout facilities has hindered this. Other areas that may need to be tackled to improve collaboration and support these initiatives include workload disparities, the number of staff, and other resources available to research and analyse different topics. This review systematically presents data regarding the collaboration of these teams. It highlights the implementation of common processes and information exchange in Main Communication Protocols and effective workflow for coordinating the care for critically ill patients as pillars for better outcomes in patient care in critical care settings.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it