<i>Evidence-Based Nursing</i>: 4 years down the road
Bibliographic record
Abstract
Evidence-Based Nursing: 4 years down the road Evidence-based practice in the nursing profession is gaining wider popularity as shown by the increasing number of nursing conferences with an evidence-based theme, journals that feature "evidence-based, best practices in healthcare", professional nursing organisations that are developing or promoting clinical practice guidelines, and subscription to this journal by over 8000 nurses from around the world.As we begin our fifth year of production of Evidence-Based Nursing (EBN), it is useful to look back at developments that have occurred over the past 4 years.In this editorial, we will describe the involvement of our many colleagues in the production of this journal, identify the journals from which we abstract the most studies, highlight the purposes of our editorials, and update our readers about our website.
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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 | 0.029 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.030 | 0.023 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.017 | 0.029 |
| Insufficient payload (model declined to judge) | 0.024 | 0.018 |
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".