Human anatomy and clinical nursing practice
Bibliographic record
Abstract
Introduction and Objective: Human anatomy is an essential component of the undergraduate nursing curriculum for learning the specific disciplines which deal with clinical practice. Anatomical knowledge provides assurance for the practice of clinical assessment and invasive procedures of legal competence of nurses. The aim of the study was to analyze the correlation of the content taught in the discipline Human Anatomy with the clinical practice of undergraduate nursing students in the discipline Semiology and Semiotics in Nursing and The Care Process, as well as their assurance to start it.Methods: Quantitative descriptive study with the application of an online questionnaire to 66 undergraduate nursing students at a public education institution in the interior of São Paulo. Data analysis by number of occurrences and Chi-square test.Results: There was partial agreement about the interdisciplinarity between human anatomy and disciplines of clinical nursing practice. The students agreed to be partially assured about the procedures to start the semiological practice of different devices and to perform nursing procedures. The predominance of the superficial approach to content related to the clinical practice of the disciplines Semiology and Semiotics in Nursing and The Care Process was predominant.Conclusions: The teaching of human anatomy, along the lines offered, maintains an unsatisfactory correlation with clinical practice due to the students’ experience, interfering with learning, acting in clinical teaching and professional training.
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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 | 0.002 | 0.008 |
| 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 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".