Vivência do acadêmico de enfermagem no setor de Triagem hospitalar
Bibliographic record
Abstract
Triage in emergency services has emerged to identify the most urgent or potentially more serious cases ensuring they receive preferential treatment relative to less urgent cases. . This study has the objec-vo to describe the experience of a nursing student during volunteer training held in a public hospital in Minas Gerais. After prior contact with the nurse in charge, gave their permission to undertake an internship supervised by the same within 45 days in risk rating industry. During this period, the scholar went through a process of updated review on the subject and perception of professional tasks of nurses. The probationary period, we realized the need for broad scientific expertise of nurses to perform correct classification, both the Canadian protocol, as in Manchester. The insight of a professional in conducting a directed interview pathology is crucial to allow for a correct flow of the patient, based on the complexity of your problem targeting priority service. The results obtained from this study demonstrate that extension actions allow valuable educational experiences, it was possible to verify the importance of risk classification, the dynamic operation of the service, facilitators and challenges to overcome. This study allows us to affirm the importance and benefits provided by the initiative of the nursing student to seek new knowledge and experiences, complementing its range of teaching and learning.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".