Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,045 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,008 | 0,004 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,005 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,246 | 0,158 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».