Education and Training for the Rapid Response System: Courses or Bedside?
Bibliographic record
Abstract
The first consensus conference on rapid response systems defined four main components namely, the afferent arm, (to identify the deteriorating patient and escalate care), the efferent arm (the responding team), a process improvement arm, and an administrative arm [1]. As a consequence, it is possible to assume that there are at least four different teams to educate and train in every established rapid response system [1]. Members of each of the four arms have different background and expectations. These teams need also to be able to integrate their knowledge in order to deliver an efficient patientcentred care. The evidence shows that there are different ways to structure a rapid response system to rescue deteriorating patients in the ward in hospitals around the world [1,2]. Moreover, the efferent arm responding to the calls needs to work efficiently engaging different health care professionals around the hospital. It is intuitive that there are no unique answers to the question “are courses better than bedside teaching?” There are multiple levels of education and training that should be offered. Knowledge must be also maintained, and this could be achieved using again either courses or bedside teaching. The goal of this manuscript is to identify the needs and the limitations of training and education provided to an established rapid response system. A structured rapid response system means a configuration reflecting the four arms defined by the experts during the first consensus conference [1]. Principles of adult learning will be presented in the context of education and training using both courses or bedside tutoring. Technology will be acknowledged given the enormous contribution that this has given to improve the activation rate and the performance of the rapid response system. Luckily, bedside teaching can be integrated with alternative solutions such as immersive learning, virtual reality and simulation This possibility is relevant in relation to time limitation during training and high costs of dedicated study time. This manuscript will explore solutions to face challenges based on rapid response systems specific needs.
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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 | 0.026 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".