Самооценката на студентите и практикуващите медицински сестри относно теоретичната им подготовка за получаване на информирано съгласие от пациентите
Bibliographic record
Abstract
Introduction: Along with the growing role of nurses, a number of challenges and unresolved issues have been determined in nursing practice, including informing and obtaining patient consent. Training nurses on the issue of informed consent is one of the ways to overcome them. Aim: The aim of this article is to examine the self-assessment of students and nursing practitioners regarding their theoretical training to obtain informed consent from patients. Materials and Methods: Attached is the analysis of literature, documentary and questionnaire method. This paper examines the opinion of 290 students, graduate nurses trained in MU - Varna and MU - Pleven, 320 nurses working in the hospitals for active treatment in Varna, Dobrich, Ruse, Silistra and Shumen. The survey including graduate students was conducted in the period 2008 - 2014. The representative survey with practicing nurses was conducted between 2010 - 2014. Results and Discussion: Half of the future and current health professionals consider their knowledge very good, and little more than a quarter of students and one fifth of healthcare professionals described it as good. At the same time, more than a quarter of nursing practitioners and a fifth of graduate students think that their knowledge is excellent. Conclusion: The knowledge of nurses on issues related to patients` informed consent is an essential factor in optimizing the process of informed consent and in attracting the patient as an active and full participant in the care process
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.008 |
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".