Determination nurses’ knowledge about initial drugs used during emergency management of acute myocardial infarction
Bibliographic record
Abstract
Objective: Acute myocardial infarction is a life threatening condition that influences the physical, psychological and social dimensions of the individual. The aim of this study was conducted to determine nurses’ knowledge of initial treatment during an emergency management of patients with acute myocardial infarction.Methods: A descriptive study was used. The standardized administered questionnaires were administered on 139 critical care nurses who are employed in five teaching hospitals at Khartoum state/Sudan. Data processed using the statistical package software (SPSS); version 19, Chi-Square test was used, p-value of < .05 considered statistically significant for the analysis.Results: The study results revealed that the total mean knowledge scores of the studied subjects related to the initial treatment of acute myocardium infarction was found to be low than bench mark with t & p values (t = 6.87 at p = .000). Also, most of studied subjects had poor level of knowledge regarding thrombolytic agents specifically about characteristics of streptokinase, indications, contraindications and complications; even among trained nurses (p values = .000).Conclusions: The studied subjects had poor levels of knowledge related to the drugs used for initial management of acute myocardial infarction specifically those related to understanding the thrombolytic agents.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".