Bibliographic record
Abstract
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
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.191 | 0.086 |
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".