Bibliographic record
Abstract
It is a great pleasure to announce that the Journal of Nursing Research (JNR) has stepped into its 6th year since being indexed in both the Journal of Citation Report/Science Edition (SCI) and Social Science Edition (SSCI). JNR is the official journal of the Taiwan Nurses’ Association (TWNA). In order to better advance and inspire active interactions and scientific inquiry within and among the fields nursing, clinical practice, and nursing education, the journal began being published in English since 2001 and was successfully indexed in SCI and SSCI in 2012. Compared with many internationally recognized nursing journals, JNR is still in its infancy and growing. However, the continuous contribution and support of nursing researchers in Taiwan and internationally has led JNR to become one of the world’s most active nursing journals, attracting manuscript submissions from nursing researchers around the world. Furthermore, JNR‘s online edition continues to enjoy a rapidly increasing readership in both Western and Eastern countries, with the United States and Taiwan, respectively, providing JNR’s first and second largest readership, followed by Turkey, the UK, Iran, Australia, Canada, India, China, and South Korea. Both submission and research data support the important role of JNR in facilitating knowledge dissemination as well as reaffirm the journal’s scientific quality and value as an academic nursing journal. JNR is committed to further enhancing the growth and depth of evidence-based knowledge in nursing science, nursing education, and clinical practice. Therefore, the journal will be published as a bimonthly instead of a quarterly publication starting this year (2017). In addition to original articles, we will be publishing a larger number of reviews and perspective papers on important issues in nursing and healthcare in order to support our readers’ needs. Research to promote health and to overcome care problems, from acute to chronic and community care, will be addressed and discussed in JNR. Innovative studies that facilitate the performance of nursing education, nursing practice, and care-system quality represent important directions for the coming year and beyond. Furthermore, using high technology and informatics to enhance the quality of future care is a formative trend in healthcare and thus an important and growing target area for JNR’s scientific works. However, as always, qualitative inquiry that captures the precious living experiences in health and in illness will remain an important part of JNR as well. Finally, patient and nurse safety and violence-prevention issues are both important concerns in our daily practice and areas that deserve new knowledge and evidence-based improvement. JNR has built a solid foundation for disseminating nursing science over its first decade in English. We strongly believe that it will continue to grow and improve as an influential academic nursing journal, supporting the expansion of critical nursing knowledge and increased interactions among nurse clinicians, educators, students, and researchers as well as advancing nursing sciences and professionalism at multiple levels and across the healthcare spectrum.
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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.651 | 0.451 |
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".