An overview of 5‐year patterns and trends in the Journal of Nursing Management
Bibliographic record
Abstract
An overview of 5-year patterns and trends in the Journal of Nursing ManagementThe Journal of Nursing Management (2019) is an international forum which aims to inform and advance the discipline of nursing management and leadership.The Journal encourages scholarly debate and critical analysis to provide evidence that may underpin and illuminate the practice of management, innovation and leadership in nursing and health care.It publishes current issues and developments in practice in the form of research papers, in-depth commentaries and analyses.The Journal welcomes papers from researchers, academics, practitioners, managers and policymakers from a range of countries and backgrounds that examine relevant issues and contribute to the body of knowledge in international nursing management and leadership worldwide.An exploration of the recent output of scholars within the Journal of Nursing Management is insightful and provides a little more information on research activity in the field and the extent to which the overarching aims of the Journal are being achieved.The aim of this editorial is to provide a brief overview of general patterns and trends in publications in the Journal of Nursing Management with a focus on the past five years (2015-2019) and to suggest ways in which this information could be utilized to inform the future strategic direction of the journal and future publications.A search within the Scopus database (2019) revealed that there have been 2,266 publications in the Journal since its foundation in 1993.The majority of these papers are research articles (78.7%, n = 1,785).There are smaller numbers of literature reviews (7.9% n = 179) and editorials (7.5%, n = 170).The remainder of the papers consists of letters, commentaries and conference papers.The top ten countries publishing most frequently in the Journal (in descending order) were the USA, the UK, Australia, Canada, Sweden, Finland, Ireland, Norway, Italy and Taiwan.Half of these are Englishspeaking countries.The most frequently published institutions were from mostly English-speaking countries
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.043 | 0.060 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".