Nurses’ Experiences with Activating Rapid Response Teams: A Qualitative Study
Bibliographic record
Abstract
Patient clinical deterioration is a major safety concern. One strategy implemented for healthcare providers to help improve the timely recognition and response to patient deterioration is the Rapid Response Team (RRT). Despite this resource, patient deterioration still occurs and delayed activation of the RRT is one contributing factor. Little is known about the perspectives and experiences of unit-level (also termed “ward”) nurses related to RRT activation, which is problematic given they are the ones who are primarily responsible for that process. The purpose of this study was to understand the experiences of nurses practising on general adult inpatient medicine units and their activation of the RRT. The research question was addressed with a descriptive, exploratory qualitative study. Nurses working on a medicine unit at an Ontario hospital study site were purposively recruited to participate. Semi-structured interviews with the six participants were held online and audio-video recorded. Inductive, thematic analysis was used. Eleven themes about the barriers and facilitators to RRT activation, and one overarching theme—The Self-Imposed Complexity of Deciding to Activate the RRT—resulted in relation to the nuanced, multi-factorial decision-making process unit-level nurses undertake when considering activation. This information will inform practice changes surrounding RRT policies, nursing education about the RRT, and be incentive for future research on optimizing strategies for RRTs and deteriorating 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 | 0.026 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".