Notice bibliographique
Résumé
PACES around the worldFigure: Anthea Court, Associate Director, Evidence Transfer and Utilisation2005 has been both a productive and interesting year. As I write this editorial, Institute staff are continuing to prepare for our first JBI Convention – Pebbles of knowledge: making evidence meaningful, to be held at the Hilton Adelaide around the time of this magazine's release. The program looks excellent and I hope to have the opportunity to see you there. I would also like to welcome Nic Rowan, our new journalist, to the Joanna Briggs Institute team, who has been working with Zoe Jordan on this issue. Thanks to all who have contributed. In this issue we meet a fascinating array of individuals contributing to evidence-based healthcare across the world. Doris Grinspun, Executive Director of the Registered Nurses Association of Ontario, shared with us her experiences of living and working in Israel, the United States and now Canada. This is a truly inspirational story. We were also privileged to talk to Liz McInnes of the National Institute for Clinical Excellence about strategies for successful implementation of evidence-based guidelines in the United Kingdom. Our interview with nurses in Africa is a compelling and moving story as they battle to improve health care in a country torn by social ravages and poverty. As an Institute, we strive to support such endeavours as best we can and hope that you, too, will be able to share your experiences at the forthcoming Joanna Briggs Colloquium in Durban, South Africa, next year (see page 39 for more details about the Colloquium). From an ‘oasis’ in the United Arab Emirates to the ‘spicy maelstrom’ of Turkey, we also explore evidence utilisation from some diverse and spectacular parts of the world. It is interesting to note not only the differences they experience, but also some of the many similarities they face in the challenge to base their practice on evidence. The Institute recognises that utilisation of the best available evidence is a process that impacts not only health professionals, but also consumers of health care. In this issue we talk with a consumer whose strength, passion and drive for better evidence-based consumer information reminds us of the need for a ‘team’ approach to using evidence. Among the many other stories in this issue, including evidence-based podiatry in Scotland and forensic mental health in Australia, we also welcome our second intake of Aged Care Clinical Fellows as they embark on their journey to improved aged care practice and experience the JBI PACES (Practical Application of Clinical Evidence) program. We look forward to following their progress in future issues. I hope that you enjoy this issue of PACEsetterS and also invite you once again to submit your international ‘picture of health’ (more details on that page 11). As we head towards the festive season I would like to take this opportunity to wish you and all our readers a safe and prosperous Christmas and New Year. Have your say We would like to know you betterFigure
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 | 0,003 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,008 | 0,003 |
| Communication savante | 0,020 | 0,012 |
| Science ouverte | 0,001 | 0,010 |
| Intégrité de la recherche | 0,004 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,191 | 0,086 |
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 ».