The Registry of Knowledge Translation Methods and Tools: a resource to support evidence-informed public health
Bibliographic record
Abstract
OBJECTIVES: This paper examines the development of a globally accessible online Registry of Knowledge Translation Methods and Tools to support evidence-informed public health. METHODS: A search strategy, screening and data extraction tools, and writing template were developed to find, assess, and summarize relevant methods and tools. An interactive website and searchable database were designed to house the registry. Formative evaluation was undertaken to inform refinements. RESULTS: Over 43,000 citations were screened; almost 700 were full-text reviewed, 140 of which were included. By November 2012, 133 summaries were available. Between January 1 and November 30, 2012 over 32,945 visitors from more than 190 countries accessed the registry. Results from 286 surveys and 19 interviews indicated the registry is valued and useful, but would benefit from a more intuitive indexing system and refinements to the summaries. User stories and promotional activities help expand the reach and uptake of knowledge translation methods and tools in public health contexts. CONCLUSIONS: The National Collaborating Centre for Methods and Tools' Registry of Methods and Tools is a unique and practical resource for public health decision makers worldwide.
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 | 0.283 | 0.432 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.058 | 0.050 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.019 | 0.030 |
| Open science | 0.008 | 0.019 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.027 | 0.027 |
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".