Rapid response systems and collective (in)competence: An exploratory analysis of intraprofessional and interprofessional activation factors
Bibliographic record
Abstract
The rapid response system (RRS) is a patient safety initiative instituted to enable healthcare professionals to promptly access help when a patient's status deteriorates. Despite patients meeting the criteria, up to one-third of the RRS cases that should be activated are not called, constituting a "missed RRS call". Using a case study approach, 10 focus groups of senior and junior nurses and physicians across four hospitals in Australia were conducted to gain greater insight into the social, professional and cultural factors that mediate the usage of the RRS. Participants' experiences with the RRS were explored from an interprofessional and collective competence perspective. Health professionals' reasons for not activating the RRS included: distinct intraprofessional clinical decision-making pathways; a highly hierarchical pathway in nursing, and a more autonomous pathway in medicine; and interprofessional communication barriers between nursing and medicine when deciding to make and actually making a RRS call. Participants also characterized the RRS as a work-around tool that is utilized when health professionals encounter problematic interprofessional communication. The results can be conceptualized as a form of collective incompetence that have important implications for the design and implementation of interprofessional patient safety initiatives, such as the RRS.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".