An assessment of cardiac nurses' knowledge and practices regarding patient safety after cardiac catheterization
Bibliographic record
Abstract
Cardiac catheterization is a medical procedure that helps doctors examine the heart and its blood vessels in detail. It involves inserting catheters (thin tubes) into a blood vessel in the arm or leg and guiding them to the heart with the help of X-ray images. This process allows for the collection of data on blood flow, pressure, and oxygen levels in the heart, as well as taking samples and imaging the heart's arteries and chambers. Ensuring patient safety, which means preventing harm during healthcare, is crucial, especially after this procedure. The team performing cardiac catheterization must prioritize safety and quality care to avoid complications. This study, the first of its kind in the Northern Region of Saudi Arabia, aims to evaluate the knowledge and practices of cardiac nurses regarding patient safety after cardiac catheterization. It employs a descriptive, correlational, cross-sectional design and analyzes data using frequency and percentages. Conducted from July to September 2023 at a Cardiac Center in Hail, Saudi Arabia, it involves nurses with varying levels of education and experience in cardiac care. The research tools include a questionnaire on potential complications post-catheterization and an observation tool for nursing care. The findings indicate that experience affects knowledge levels, with nurses having over five years of experience showing higher knowledge. The study also finds that most nurses have moderate to adequate knowledge about patient safety post-catheterization, but there's room for improvement. Compared to similar studies, this research underscores the importance of experienced nursing care in preventing complications. It suggests the need for enhanced training and education focusing on care after cardiac catheterization to improve patient outcomes.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".