Challenges in implementing the rapid response team: integrative review
Bibliographic record
Abstract
Introduction: The rapid response team is formed in hospitals with the aim of promoting the reduction in the occurrence of adverse events and reducing the risk and damage, disability or death of patients admitted to wards. These teams work to act quickly and early in situations of clinical deterioration in hospital wards, generally indicated by the use of early warning scales, an example being the MEWS score. The implementation of these teams, however, faces challenges and the extent of this problem in our country is not known for sure. Method: This study sought to identify and analyze scientific evidence on the challenges of implementing the rapid response team, according to the available literature. An integrative review was carried out based on the guiding question 'what is the evidence on the challenges of implementing the rapid response team, according to the scientific literature' in the databases Medical Literature Analysis and Retrievel System Online (MEDLINE), Latin American Literature and Caribbean Health Sciences (LILACS) and Nursing Database (BDENF), in addition to Google Scholar, in order to detect articles not indexed in the English and Portuguese languages, published in the last 5 years and available in full. Results: The sample included 6 articles published between 2018 and 2022, from Canada, Australia, Brazil and Korea. The main challenges encountered were the lack of exclusive human resources (doctors and nurses) and the lack of training of ward professionals. Furthermore, the challenge of establishing an assertive trigger to activate the team is discussed, in addition to the need and financing for software that generates automatic alerts based on the insertion of clinical data. Conclusion: It is concluded that regardless of monitoring technologies, the relevance of checking vital signs in a safe, periodic manner, with immediate records, prevails. Furthermore, the need for a work process focused on the use of scores that direct team actions in an assertive manner was highlighted. This study reinforced the importance of periodic training of teams, and also of teams in general, about the purpose of the team, in order to promote communication and coordination between teams in favor of patients.
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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 | 0.014 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.019 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".