Translating research for evidence-based public health: key concepts and future directions
Bibliographic record
Abstract
Applying research to guide evidence-based practice is an ongoing and significant challenge for public health. Developments in the emerging field of 'translation' have focused on different aspects of the problem, resulting in competing frameworks and terminology. In this paper the scope of 'translation' in public health is defined, and four related but conceptually different 'translation processes' that support evidence-based practice are outlined: (1) reviewing the transferability of evidence to new settings, (2) translation research, (3) knowledge translation, and (4) knowledge translation research. Finally, an integrated framework is presented to illustrate the relationship between these domains, and priority areas for further development and empirical research are identified.
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 | 0.220 | 0.153 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.005 | 0.057 |
| Scholarly communication | 0.025 | 0.064 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.019 | 0.017 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".