A scoping review reveals candidate quality indicators of knowledge translation and implementation science practice tools
Bibliographic record
Abstract
OBJECTIVES: To identify candidate quality indicators from existing tools that provide guidance on how to practice knowledge translation and implemenation science (KT practice tools) across KT domains (dissemination, implementation, sustainability, and scalability). STUDY DESIGN AND SETTING: We conducted a scoping review using the Joanna Briggs Institute Manual for Evidence Synthesis. We systematically searched multiple electronic databases and the gray literature. Documents were independently screened, selected, and extracted by pairs of reviewers. Data about the included articles, KT practice tools, and candidate quality indicators were analyzed, categorized, and summarized descriptively. RESULTS: Of 43,060 titles and abstracts that were screened from electronic databases and gray literature, 850 potentially relevant full-text articles were identified, and 253 articles were included in the scoping review. Of these, we identified 232 unique KT practice tools from which 27 unique candidate quality indicators were generated. The identified candidate quality indicators were categorized according to the development (n = 17), evaluation (n = 5) and adaptation (n = 3) of the tools, and engagement of knowledge users (n = 2). No tools were identified that appraised the quality of KT practice tools. CONCLUSIONS: The development of a quality appraisal instrument of KT practice tools is needed. The results will be further refined and finalized in order to develop a quality appraisal instrument for KT practice tools.
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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 | 0.170 | 0.480 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.026 | 0.028 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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; 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".