Bibliographic record
Abstract
One of the most exciting sessions at the III Step Conference took place on the last day. Among the presenters, Carol Richards, (JNPT's own consulting Editor) presented the Canadian experience with knowledge translation (KT). She defined KT as “turning the best evidence into practice” and described the Canadian Stroke Network, in which dozens of clinicians and scientists work together standardizing care delivery and documentation. We were on the edge of our seats as she described the program and shared some of the online resources for efficient evidence retrieval. Many in the room lamented the fact that they were not in Canada, working in such a cohesive rehabilitation care delivery system. Some fantasized about sabbaticals in Canada. Others began to brainstorm about how we could learn from our Canadian colleagues and create our own efforts at KT. Those of us from JNPT were strategizing about how to best deliver the journal content to our colleagues in research, education, and practice. How could we create opportunities to discuss, digest, and ultimately, apply the findings reported in the journal and other literatures relevant to our specialization. Our first attempt at KT is now a reality. The March 2006 issue of JNPT features podcasts of each article's abstract. JNPT has joined with Science Audio productions introducing this innovation into publication. In fact, we are the only journal indexed in MEDLINE that provides such a service. Yes, I guess we are very excited about this development. Here is how it works: On the on-line March JNPT table of contents for each article there is an icon symbol that allows you to select a MP3 file. You can download the file into your MP3 player, computer, or ipod. While you are multi-tasking you can listen to the abstract of each article and decide if you want to read the entire thing. That is not all. We will also have abstracts, as well as commentaries and discussions with the authors. In this issue, the lead article by Perry and colleagues is commented on by a scientist and a clinician. They converse with the author about the relevance of the findings to practice!SymbolThere is more. We have also included in the March issue companion videos. This is not an innovation as we appended videos to our issue on technology published over 3 years ago. It is however a re-statement of how important it is for the journal to use technology to enhance KT. The video connected to the March JNPT issue complements the article on Taiji buddies and PD. Examples of the Taiji forms are included as figures in the print version and videos are found on the on-line version of JNPT. We are planning many more forums where our authors and readers can interact. Now that we have a mechanism for KT, we look forward to submissions that will indeed be regarded as best evidence.
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 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.088 | 0.052 |
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".