EVIDENCE-BASED PRACTICE TEACHING INTEGRATION IN COLLEGE NURSING STUDIES: HISTORICAL AND LEGAL ASPECTS
Bibliographic record
Abstract
To better understand and analyze the education of evidence-based practice, it is necessary to review the context and premises of the formation of evidence-based practice itself. The beginning of the evidence-based practice movement in the teaching of health professionals’ dates back to 1910 and looking at the health care system; first, there was evidence-based medicine. In 1990, Sackett's initiative at McMaster University in Canada, it was decided to change the term "evidence-based medicine" to "evidence-based practice" (Mackey Bassendowski, 2017; Thom Eaves, 2015). In nursing, the beginning of evidence-based practice is linked to the reforms of nursing science and practice by the first nursing researcher, Florence Nightingale, from 1854 to 1910, although the term of “evidence-based practice” was not yet known at the time. The professional training of nurses as one of the leading health professionals is related to health policy and the development of nursing science. The European Qualifications Framework (EHEA) defines the expected learning outcomes for professionals with a bachelor's degree, including the skills to find, evaluate, reflect, and apply scientific information in practice (Bologna Working Group, 2005). Despite international and national recommendations, it is difficult for many higher education institutions to refine the steps of teaching evidence-based practice in nursing study programs. These difficulties are revealed by the ambiguity in the definition of the concept of evidence-based practice (Horntvedt et al. 2018).The problematic question is: What is the basis for integrating evidence-based practice training into college nursing study programs?The study is based on the scientific literature review.
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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 | 0.042 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.008 | 0.030 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".