WhatisKT wiki: a case study of a platform for knowledge translation terms and definitions — descriptive analysis
Bibliographic record
Abstract
BACKGROUND: More than a hundred terms, often with unclear definitions and varying emphases, are used by health research and practice communities across the world who are interested in getting the best possible evidence applied (e.g., knowledge translation, implementation science, diffusion of innovations, and technology transfer). This makes finding published evidence difficult and can result in reduced, misinterpreted, or challenging interactions among professionals. Open dialogue and interaction among various professionals is needed to achieve consolidation of vocabulary. We use case report methods to describe how we sought to build an online tool to present the range of terms and facilitate the dialogue process across groups and disciplines interested in harnessing research evidence for healthcare. METHODS: We used a wiki platform from Wikispaces to present the problem of terminology and make a case and opportunity for collaboration on usage. Wikis are web sites where communities of users can collaborate online to build content and discuss progress. We gathered terms related to getting research into practice, sought published definitions, and posted these on the wiki (WhatisKT http://whatiskt.wikispaces.com/). We built the wiki in mid-2008 and promoted it through various groups and publications. This report describes the content of the site, our promotion efforts, use of the site, and how the site was used for collaboration up to the end of 2011. RESULTS: The WhatisKT wiki site now includes more than 120 pages. Traffic to the site has increased substantially from an average of 200 monthly visits in 2008 to 1700 in 2011. Visitors from 143 countries viewed the wiki in 2011, compared with 12 countries in 2008. However, most use has been limited to short term accesses of about 40 seconds per visit, and discussion of consolidation and solidifying terminology is conspicuously absent. CONCLUSIONS: Although considerable interest exists in the terms and definitions related to getting research into practice based on increasing numbers of accesses, use of the WhatisKT wiki site for anything beyond quick lookups was minimal. Additional efforts must be directed towards increasing the level of interaction among the members of the site to encourage collaboration on term use.
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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 | 0.023 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".