Bibliographic record
Abstract
One of the greatest challenges for public health researchers and practitioners is the translation of knowledge into evidence-based programs and policies. University-based research yields a growing supply of new discoveries, and practitioners, payers, and consumers are eager to benefit from this science. However, clinical and public health research findings are often “lost in translation” for fifteen to twenty years before their incorporation into practice. This translation gap prevents many nations from reaping the benefits of billions of dollars of funds spent on research, and it frustrates researchers and funders who are motivated to improve real-world practice. Over the past several years, much more attention has been paid to translational research in mainline medical and public health journals. Similarly, federal agencies and foundations are beginning to support translational research more fully. For example, recent funding announcements from the US National Institutes of Health show the higher priority being placed on translational research. Similarly, the Canadian Institutes of Health Research has increased its emphasis on funding translational research. This article provides a collection of resources that offer insight into the translation of science to practice and policy, organized into seventeen sections that are designed to highlight the key issues involved in shortening the translation gap.
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 | 0.135 | 0.407 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.034 | 0.032 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.015 | 0.017 |
| Insufficient payload (model declined to judge) | 0.043 | 0.017 |
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".