Bibliographic record
Abstract
We developed a framework for assessing country-level efforts to link research to action. The framework has four elements. The first element assesses the general climate (how those who fund research, universities, researchers and users of research support or place value on efforts to link research to action). The second element addresses the production of research (how priority setting ensures that users' needs are identified and how scoping reviews, systematic reviews and single studies are undertaken to address these needs). The third element addresses the mix of four clusters of activities used to link research to action. These include push efforts (how strategies are used to support action based on the messages arising from research), efforts to facilitate "user pull" (how "one-stop shopping" is provided for optimally packaged high-quality reviews either alone or as part of a national electronic library for health, how these reviews are profiled during "teachable moments" such as intense media coverage, and how rapid-response units meet users' needs for the best research), "user pull" efforts undertaken by those who use research (how users assess their capacity to use research and how structures and processes are changed to support the use of research) and exchange efforts (how meaningful partnerships between researchers and users help them to jointly ask and answer relevant questions). The fourth element addresses approaches to evaluation (how support is provided for rigorous evaluations of efforts to link research to action).
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 | 0.437 | 0.553 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.036 | 0.032 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.005 | 0.031 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".