A Survey of Nurses' Beliefs About the Medical Emergency Team System in a Canadian Tertiary Hospital
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it