From new kid on the block to leading journal: a review and reflection on the first 20 years of the <i>European Journal of Cardiovascular Nursing</i>
Bibliographic record
Abstract
BACKGROUND: This year marks the 20th birthday of the European Journal of Cardiovascular Nursing (EJCN). The official journal of the Association of Cardiovascular Nursing and Allied Professionals, is now recognized as one of the leading nursing and allied professional journals. AIMS: This article reflects on the developments and impact of the journal over its 20-year lifespan. METHODS AND RESULTS: We present a descriptive account of the journal from inception (2002) until present day (2021), using data provided by the EJCN editorial office and extracted from published and available information. In the last 20 years, the EJCN has published 20 volumes, 106 issues, and 1320 papers from 79 countries. The volume and quality of papers has been consistently increasing, culminating in a 2020 impact factor of 3.908, the highest in its history, ranking second for nursing science. Papers are predominantly patient focused with a range of research methods that cover an extensive range of cardiovascular conditions. Authors who contributed to the first issue continued their contribution; 293 articles in total. CONCLUSION: The EJCN has evolved into a leading journal of cardiovascular care. As the journal enters its next era, with a new Editor-in-Chief, it is appropriate to have reflected on the phenomenal contribution of the outgoing Editor-in-Chief, and the editorial team, over the last 20 years.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.075 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.007 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".