Type and Extent of Knowledge Translation Resources Published by Peer-Reviewed Rehabilitation Journals
Bibliographic record
Abstract
© 2015 by Begell House, Inc. We describe the extent and type of knowledge translation (KT) resources that are published by peer-reviewed rehabilitation journals. Rehabilitation journals traditionally disseminate new knowledge to the scientific community via scholarly publications. Principles of KT suggest that the uptake of research evidence into practice can be improved if research results are readily available and customized to end-users. Using the Google search engine, we identified 50 rehabilitation journals, and data were extracted for the type and amount of KT resources published in 2012. KT resources were classified using the taxonomy of KT interventions. Of the 50 rehabilitation journals, 31 had resources that fall within the domain of KT, which were mostly systematic reviews (21/31). The total number of KT resources per journal ranged from 0 to 55, and the amount of types published in specificjoumals ranged from 0 to 6. Systematic reviews (30/31) followed by podcasts (6/31) and videos (5/31) were the most common KT resources. All journals that published these resources targeted healthcare professionals (HCP) (31/31); only one journal, the Journal of Orthopedic and Sports Physical Therapy, published KT for the general public. We found that rehabilitation journals focus on the dissemination of scientific papers and have few KT resources for the general public and policy makers.
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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 | 0.043 | 0.341 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.098 | 0.096 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.010 |
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".