Bibliographic record
Abstract
The general purpose of Evidence-Based Nursing is to select from the health related literature those articles reporting studies and reviews that warrant immediate attention by nurses attempting to keep pace with important advances in their profession. These articles are summarised in “value added” abstracts and commented on by clinical experts. The specific purposes of Evidence-Based Nursing are: The Royal College of Nursing (RCN) Publishing Company and the British Medical Journal (BMJ) Publishing Group publish Evidence-Based Nursing under the editorship of Dr Alba DiCenso and Dr Donna Ciliska at McMaster University in Canada and Dr Nicky Cullum at the University of York in the UK. The Health Information Research Unit (HIRU) of the Department of Clinical Epidemiology and Biostatistics at McMaster University hosts the editorial office for the production of the abstracts and commissioning of commentaries. Dr Brian Haynes acts as coordinating editor to ensure that methods and procedures are consistent with other evidence-based journals prepared by HIRU. All articles in a journal issue are considered for abstracting if they meet these criteria: ### BASIC CRITERIA
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 | 0.052 | 0.136 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.125 | 0.034 |
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".