Nurses’ Practices and Lead Selection in Monitoring for Myocardial Ischemia
Bibliographic record
Abstract
BACKGROUND: The 5-lead electrocardiogram (ECG) provides key information, including clues that a patient may be experiencing myocardial ischemia, usually demonstrated in the ST segment. Studies have shown that nursing knowledge regarding ischemia monitoring is suboptimal, even though national guidelines for ECG monitoring were published in 2004 by the American Heart Association and endorsed by the American Association of Critical Care Nurses. PURPOSE: The aims of this study were to identify best practice regarding 5-lead ECG myocardial ischemia monitoring, assess current unit-level practice at 1 institution, and to educate nurses on proper monitoring using a nurse-led, evidence-based intervention. METHODS: The authors created an educational PowerPoint designed to educate nurses on proper lead selection to monitor the ST segment for patients admitted with known or suspected myocardial ischemia and developed a 3-part online survey to assess current unit practice and to assess knowledge before and after intervention. RESULTS: A total of 18 registered nurses (RNs) completed the survey. Results indicated that RNs lacked knowledge regarding continuous ECG monitoring for ischemia and had room for improvement in their everyday practice habits. The knowledge preintervention test mean score (out of 9) was 3.11 (SD, 1.68), and the postintervention test mean score was 6.94 (SD, 1.55), which was significant (P = .000). The intervention also significantly improved the monitoring comfort level of RNs, with a preintervention comfort level of 2.53 (SD, 1.07) and a postintervention level of 3.41 (SD, 1.00) (P = .007). The process allowed the authors to reflect on the key steps of implementing evidence-based projects in nursing units. CONCLUSIONS: Continuous, 5-lead ECG monitoring is an active process that requires clinical decision making by the nurse and is not a passive activity. Registered nurses in this sample demonstrated a lack of knowledge regarding ECG monitoring for ischemia that was improved with an online educational intervention and reported intentional daily practice pattern changes postintervention testing. A unit-level intervention driven by nurses may be successful at improving fellow RNs' knowledge and evidence-based practice.
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.
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 | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".