Identifying Candidate Quality Indicators of Knowledge Translation Practice Tools that Support the Practice of Sustainability: A Scoping Review
Bibliographic record
Abstract
Knowledge translation (KT) and implementation science (IS) aim to bridge the research gap between what we know (research evidence) and what we do (practice and policy) in health. KT practice tools (KT-PTs) provide methods guidance across a wide range of KT domains such as dissemination, implementation, sustainability, scalability, and IKT. There is limited evidence-based guidance to determine which KT-PTs are the most relevant for their needs and help assess the overall quality of KT-PTs. As a first step, a scoping review was conducted to identify candidate quality indicators of KT-PTs, focusing on the sustainability domain of KT. The scoping review was guided by the Joanna Briggs Institute methodology for scoping reviews. The review identified and characterized 35 KT-PTs. 17 candidate quality indicators were identified across four categories. Findings of this scoping review will guide next steps and future studies to develop and evaluate a quality assessment tool for KT-PTs.
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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 | 0.320 | 0.574 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.048 | 0.049 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".