Capacity development for knowledge mobilization: a scoping review of the concepts and practices
Bibliographic record
Abstract
Abstract There is a growing emphasis worldwide on the use of knowledge mobilization (KMb) to improve policies and practices with the latest research evidence. This emphasis calls upon knowledge producers (e.g., university researchers) to produce more relevant evidence, and knowledge users (e.g., practitioners) to access and apply evidence. However, doing KMb can be challenging for these groups without effective support and training. Therefore, individuals and organizations are undertaking capacity development interventions to facilitate the KMb process with more effective support structures, skills, and incentives. Despite its recognized importance, theoretical evidence and practical guidance on capacity development for KMb are scattered across disciplines and practices. To address this, we conducted a scoping review study to review the current practices and concepts and identify significant gaps. One-thousand six-hundred thirty records were gathered, and 105 peer-reviewed and gray literature documents from 2010 to 2020 were reviewed. Two reviewers worked independently in screening the records, and one researcher analyzed the retained documents. The analysis reveals that capacity development for KMb is a multidimensional and multiscalar concept and practice with a diverse range of initiators, initiatives, and beneficiaries. This study also reports on three thematic areas of significance emerging from the literature, namely: (a) individuals’ and organizations’ challenges in doing and supporting KMb, (b) the capacities and supports deemed needed for effective KMb, and (c) the strategies being used for delivering capacity development. Furthermore, this study identifies evidence gaps related to the process aspects of capacity development for KMb (i.e., planning), capacity development initiatives being undertaken in developing country contexts, and results from more formal evaluations of KMb capacity-building effectiveness.
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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 | 0.107 | 0.208 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.066 | 0.067 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| 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".