Examination of tools associated with the evaluation of knowledge uptake and utilization: A scoping review
Bibliographic record
Abstract
Knowledge transfer and exchange (KTE) has become an integral part of organizational practice. Evaluation of KTE, as well as knowledge products generated through this process, is important for understanding the effectiveness of KTE strategies. This scoping review aimed to identify tools and frameworks used to evaluate knowledge uptake and utilization (KUU). The search strategy included review of PubMed and Scopus databases, hand searching of relevant journals, and citation tracing. Over 6500 abstracts were screened; 292 full-text articles were shortlisted by two reviewers. Seventy-two articles described tools for evaluating KUU. A total of 23 tools could be generally applied to knowledge products/processes used in different sectors; 36 evaluation tools were designed for specific knowledge products (i.e., websites); 9 tools were discipline-specific (i.e., medical field), and four articles described evaluations of knowledge products/processes using alternative methods such as Google Analytics or qualitative methods. The majority of tools (n = 40, 56 %) focused on usability of a knowledge product or process. This scoping review identified various tools being used to assess the effectiveness and impact of KTE processes/products, however, the measures were as varied as the projects, and were often not designed to evaluate KTE in particular.
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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 | 0.186 | 0.401 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.072 | 0.055 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.002 |
| 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".