Communication and Relationship Skills for Rapid Response Teams at Hamilton Health Sciences
Bibliographic record
Abstract
Rapid response teams (RRT) are an important safety strategy in the prevention of deaths in patients who are progressively failing outside of the intensive care unit. The goal is to intervene before a critical event occurs. Effective teamwork and communication skills are frequently cited as critical success factors in the implementation of these teams. However, there is very little literature that clearly provides an education strategy for the development of these skills. Training in simulation labs offers an opportunity to assess and build on current team skills; however, this approach does not address how to meet the gaps in team communication and relationship skill management. At Hamilton Health Sciences (HHS) a two-day program was developed in collaboration with the RRT Team Leads, Organizational Effectiveness and Patient Safety Leaders. Participants reflected on their conflict management styles and considered how their personality traits may contribute to team function. Communication and relationship theories were reviewed and applied in simulated sessions in the relative safety of off-site team sessions. The overwhelming positive response to this training has been demonstrated in the incredible success of these teams from the perspective of the satisfaction surveys of the care units that call the team, and in the multi-phased team evaluation of their application to practice. These sessions offer a useful approach to the development of the soft skills required for successful RRT implementation.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".