Bibliographic record
Abstract
This article explores the concept of evidence-based nursing education. Because nurse educators incorporate evidence-based practice as a basic tenet of their programs, they assume nursing education itself is evidence based. Nursing education has a body of knowledge on which nurse educators base teaching, educational strategies, and curricular designs, but most of this knowledge is tacit, experiential, and based on practice. This knowledge relates to the art of teaching in nursing and can warrant the practice of nurse educators. However, research is also necessary to demonstrate the effectiveness of teaching approaches and strategies. Nurse educators need to develop the science of nursing education through qualitative and quantitative research, to add to the tacit knowledge underpinning nursing education strategies. When the science of nursing education is adequately developed through rigorous research, we will truly be able to say that nursing education is evidence based. Until then, it may be only a myth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.120 | 0.186 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.004 | 0.049 |
| Scholarly communication | 0.024 | 0.048 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.016 | 0.043 |
| Insufficient payload (model declined to judge) | 0.003 | 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".