Usefulness of physical examination in the professionalpractice of nurses
Bibliographic record
Abstract
Introduction: Performing a physical examination of a patient is one of the basic clinical competencies in nursing practice.It forms the basis for preparing nursing students for practice.Obtaining subjective and objective data through physical assessment enables nurses to make a diagnosis of the clinical condition of patients.The physical examination is primarily aimed at assessing the condition of the patient, so that an accurate nursing diagnosis can be made and appropriate measures can be taken to correct the problems that accompany the patients.The ability to conduct a physical examination enables better cooperation with other members of the therapeutic team.It is also believed that the ability of nurses to conduct a physical examination significantly enhances the competence of nurses.The aim of this paper was to assess the usefulness of physical examination in daily professional practice according to nursing staff.Material and methods: The study was conducted in a group of 186 nurses employed at a hospital in Podkarpackie voivodeship in Poland.The study was conducted in May 2023.The study used a diagnostic survey method with the use of a proprietary survey questionnaire.Participation in the study was random, anonymous, and voluntary.Results and conclusions: 35.9% of the nurses surveyed claimed they did not perform physical examinations very often.In contrast, 24.3% of the respondents said they performed them very often.16.5% of respondents said they performed them several times a day.The majority of respondents believed that they were the right people to perform physical examinations.According to the respondents, physical examination is an important part of a nurse's work and has a great impact on patient treatment.Too infrequent performance of physical examinations among nurses is due to work overload and additional duties, as well as lack of confidence.
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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 | 0.009 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".