Bibliographic record
Abstract
It is almost four years ago that I first heard about the call for applications for an Editor for the International Nursing Review (INR). I knew this official journal of the International Council of Nurses (ICN) had existed for a long time, I didn’t know it was over 70 years old. I knew the INR was an ICN publication; I didn’t know that ICN had partnered with Blackwell Publishing to copublish the ‘new’ journal. I knew the journal had a goal of keeping nurses worldwide in touch with one another; I didn’t know that the editorial process would require the use of technology to link staff members, peer reviewers and authors living in many different countries. The knowing and unknowing was not what led me to seek the position of Editor of INR. What intrigued me was ICN's plan to re-design and re-launch this venerable journal as a quarterly peer-reviewed publication. I wanted to be a part of that change. In April 1999 I was appointed Editor and began the journey of re-designing the journal, along with the staff of ICN and a supportive international editorial board. The systems design was most challenging as we worked from different countries via e-mail, telephone and fax. With editorial board members in 10 different time zones we were never all awake at the same time. Sometimes technology solved our problems and occasionally it caused them. Ultimately a smooth operation was put in place. The first issue of the re-launched INR became a reality in March 2000. Initially members of the editorial board served as reviewers. Later a bank of over 100 expert nurses joined the INR Peer Review Panel and they continue to serve as excellent referees of the manuscripts we receive from every corner of the world. Since the re-launch we have published authors from Sierra Leone, China, Norway, Israel, Swaziland, South Africa, Finland, Canada, Brazil and Jamaica, and many more countries where the skill and knowledge of nurses is being investigated, improved and documented in writing. As I retire from this wonderful experience I want to thank so many people for their time and energy in getting this project up, running and successful. Linda Carrier-Walker, Jan Harrington and Griselda Campbell were most helpful in the beginning starts and stops, and all along the way. Thanks too to members of the editorial board who always responded when needed, and special kudos to peer reviewers who gave time and careful feedback to authors. Grateful thanks go to every author who sent us a manuscript. It is an honour to read the work of nurses from around the world. It is inspirational to learn about the struggles and successes of nurses as they work to improve their practice, education and working conditions. Keep sending your manuscripts to the INR. Nurses of the world need to hear from all of you who have new ideas, new research, and new insights to share. And finally, my thanks to all the readers and subscribers of the International Nursing Review. It is for you that this journal exists. Vivien De Back, RN, PhD, FAAN, Editor
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 | 0.005 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.246 | 0.158 |
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".