Relationship of Nurse's Knowledge Level Towards Compliance with SOP of Post Radial Sheath Revocation Post Heart Catherization
Bibliographic record
Abstract
Coronary heart disease (CHD) is a condition in which the coronary arteries are narrowed, blocked, or abnormal in the coronary arteries. One of the measures to diagnose CHD is cardiac catheterization (Coronary Angiography), which is an invasive diagnostic procedure using access called sheath. Knowledge of Standard Operating Procedures (SOP) for post-sheath removal after cardiac catheterization is very important. Knowledge is considered one of the factors that can affect the success rate of post-radial sheath removal treatment. The purpose of this study was to determine the relationship between the level of knowledge of nurses and adherence to SOPs for post-radial sheath removal after cardiac catheterization in the cardiac care room at Tarakan Hospital, Jakarta. The study used an analytic observation method with a cross-sectional approach. The research subjects were all nurses in the heart care room at Tarakan Hospital. Samples were taken with a total sampling of 30 respondents. Data collection was carried out using a questionnaire. Correlation test using Kendall-Tau, the correlation between the two variables is 0.010 while the sig (2-tailed) number is 0.17 > greater than a = 0.05, meaning that it can be concluded that there is a relationship between the level of knowledge of nurses and adherence to SOPs for post-revocation care. radial sheath after cardiac catheterization in the cardiac care room at Tarakan Hospital, Jakarta.
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 | 0.001 | 0.011 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".