The nurse’s competencies in relation to the mobile emergency care service – SAMU
Bibliographic record
Abstract
Introduction: In Brazil, emergency and emergency care networks are supported and regulated by the unified health system, the nursing professional is essential for the practices of care performed by mobile emergency care services (SAMU ), not only with regard to guidance and medical support, it is noted that nursing has a large number of functions related to its care in SAMU. Objectives: To identify nurses’ work in relation to the Mobile Emergency Care Service. Method: A systematic literature review was conducted, with analysis of the main nursing journals in the Scientific Electronic Library Online databases (SciELO), Latin American and Caribbean Literature in Health Sciences (Lilacs) and the Online System for Search and Analysis of Medical Literature (Pubmed/MEDLINE). A total of 47 articles were collected with the theme of the study. Results: It was evidenced that there are a considerable amount of articles related to the nurse’s competencies vis-at SAMU, and the relevance of the profession for the proper functioning of SAMU. Conclusion: It is concluded that through the scientific and technical development of the profession, with the realization of undergraduate and graduate courses, nursing attributions in relation to SAMU are considered important, but that further research is still needed by students of this category. Keywords: Nursing, Mobile Emergency Care Service, nursing assignments.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".