Bibliographic record
Abstract
Figure: Zoe Jordan, Associate DirectorWelcome to the final issue of PACEsetterS for 2010. This issue comes at the end of what has been a very busy year full of change and development for the Joanna Briggs Institute. Changes in life, as we all know, can be equally scary and exciting. The same is true of change when experienced in clinical environments and it is by no means an easy feat if you are successful. In this issue of PACEsetterS we have some great stories that address aspects of knowledge translation; that is, getting evidence into practice. Many terms have been used to describe this process (implementation science, research utilisation, dissemination, diffusion, transfer, to name but a few!). Whether you are a policy maker, manager or clinician, getting research evidence into practice is difficult! Dr Ian Graham and his team at the Canadian Institutes of Health Research have invested significant time and energy examining this very challenge, as has Dr Peter Tugwell in looking at an ‘equity oriented’ approach to knowledge translation. Of course we also have our regular columns, such as RE: Members and COnNECT the Dots, which have proven to be such favourites with readers, providing great tips on JBI membership and clinical information. Although we will have a new partnership with Wolters Kluwer Health in the New Year, we would still love to hear from readers and encourage you to contribute. Our new partnership will mean new sections in PACEsetterS including ‘News and Views’, which will continue to contain all of our interviews and stories, ‘Evidence in Action’, to which we would encourage you to submit articles for peer review and publication, and ‘JBI World News’ to keep you up to date on all the activity of the Institute and our Collaboration. There is much in store for the year ahead, so keep an eye out for information on our events, such as the 2011 JBI International Convention, Mission Impossible? Evidence-based practice and the future of global health to be held in Adelaide on the 7–9th November. As we approach the festive season I would like to take this opportunity to wish all of our readers a safe and happy holiday and we look forward to bringing you a new and expanded version of our popular publication next year in partnership with Wolters Kluwer Health.
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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.461 | 0.293 |
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".