FAKTOR YANG BERHUBUNGAN DENGAN KETEPATAN PELAKSANAAN TRIASE MODERN CANADIAN TRIAGE ACQUITY SYSTEM (CTAS)
Bibliographic record
Abstract
Some patients who visit the Emergency Department (IGD) are not in an emergency condition, one way to sort it out is by triage. There are still many hospitals that are not appropriate in implementing triage, increasing the risk of death and disability of emergency patients. The purpose of this study was to determine the factors related to the accuracy of the implementation of modern triage CTAS. This research was conducted at the IGD of Bengkalis Regional Hospital. The total population is 20 emergency room nurses, with sampling using the total sampling technique. The analysis used is frequency distribution and chi square statistical test. The results of this study concluded that: Ada significant relationship between the level of knowledge of nurses and the accuracy of the implementation of the Modern Triage of the CTAS system (p value 0.002); Ada significant relationship between nurse skills and the accuracy of the implementation of the Modern Triage of the CTAS system (p value 0.011); Ada significant relationship between the perception of nurse workload and the accuracy of the implementation of the Modern Triage of the CTAS system (p value 0.00 0); Ada significant relationship between the length of work of the nurse and the accuracy of the implementation of the Modern Triage of the CTAS system (p value 0.003); Asignificant relationship between nurse training and the accuracy of the implementation of the Modern Triage CTAS system (p value 0.033). This study recommends improving the competence of all emergency room nurses, particularly through BTCLS training and modern triage of the CTAS system.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".