Towards a common terminology: a simplified framework of interventions to promote and integrate evidence into health practices, systems, and policies
Bibliographic record
Abstract
BACKGROUND: A wide range of diverse and inconsistent terminology exists in the field of knowledge translation. This limits the conduct of evidence syntheses, impedes communication and collaboration, and undermines knowledge translation of research findings in diverse settings. Improving uniformity of terminology could help address these challenges. In 2012, we convened an international working group to explore the idea of developing a common terminology and an overarching framework for knowledge translation interventions. FINDINGS: Methods included identifying and summarizing existing frameworks, mapping together a subset of those frameworks, and convening a multi-disciplinary group to begin working toward consensus. The group considered four potential approaches to creating a simplified framework: melding existing taxonomies, creating a framework of intervention mechanisms rather than intervention strategies, using a consensus process to expand one of the existing models/frameworks used by the group, or developing a new consensus framework. CONCLUSIONS: The work group elected to draft a new, simplified consensus framework of interventions to promote and integrate evidence into health practices, systems and policies. The framework will include four key components: strategies and techniques (active ingredients), how they function (causal mechanisms), how they are delivered (mode of delivery), and what they aim to change (intended targets). The draft framework needs to be further developed by feedback and consultation with the research community and tested for usefulness through application and evaluation.
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 | 0.270 | 0.170 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.025 | 0.014 |
| Science and technology studies | 0.009 | 0.067 |
| Scholarly communication | 0.021 | 0.028 |
| Open science | 0.013 | 0.018 |
| Research integrity | 0.014 | 0.022 |
| Insufficient payload (model declined to judge) | 0.004 | 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".