A Survey of Nurses' Beliefs About the Medical Emergency Team System in a Canadian Tertiary Hospital
Bibliographic record
Abstract
BACKGROUND: Nurses are the primary activators of the medical emergency team (MET). Although the MET system can empower nurses to seek help in managing acutely ill patients, few data on nurses' beliefs about the system are available. OBJECTIVE: To evaluate nurses' beliefs and behaviors about the MET system. METHODS: Nurses from a large academic hospital in Canada were surveyed (2 demography-related questions and 17 Likert-scale questions). RESULTS: Of 614 nurses employed on units participating in the MET system, 293 (47.7%) were approached and 275 completed the survey (response rate, 93.9%). Most respondents (84.2%) believed that the MET could prevent cardiopulmonary arrest in acutely ill patients, and 94% believed that the MET allowed them to seek help for patients they were worried about. Most nurses (75.9%) would call the responsible physician before activating the MET. Fifteen percent indicated reluctance to activate the MET because of fear of criticism, but only 2.2% considered the MET overused. Most (81.3%) believed that the MET did not increase their workload, and 91.3% did not believe that the MET reduced their skills. Forty-eight percent of nurses indicated that they would activate the MET for a patient they were worried about, even if the patient had normal vital signs. CONCLUSION: Nurses value the MET system. Nurses believe that the MET can help them care for acutely ill patients and improve outcomes. However, barriers to MET activation exist, including a fear of criticism and an adherence to a more traditional model of first contacting the responsible physician before activating the MET.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".