Supporting partnerships in knowledge mobilization: what existing implementation strategies can tell us
Bibliographic record
Abstract
BACKGROUND: The need for partnership between knowledge producers and knowledge users to foster effective implementation is well-established in the implementation science literature. While many theories, models, and frameworks (TMF) have been developed to guide knowledge mobilization (KM) activities, seldom do these frameworks inform approaches for establishing and maintaining KM partnerships (i.e., relationships between researchers and individuals with relevant expertise in KM activities). Thus, there is a significant knowledge-to-action gap related to operationalizing engagement in partnerships and leveraging the evidence that exists to support them. Given the abundance of TMFs, it is prudent to consider whether any may be suitable to inform approaches to partnership. The aim of this commentary is to discuss the necessity for strategies to support engagement in partnerships for KM activities, as well as to explore the potential to apply strategies from an existing implementation taxonomy to inform partnerships approaches in KM. MAIN BODY: Using a case study, this commentary explores the opportunity to apply existing implementation strategies put forward by the Expert Recommendations for Implementing Change (ERIC) taxonomy to inform partnership strategies. This case study utilized qualitative evidence from a qualitative study about KM in children's pain management informed by the Consolidated Framework for Implementation Research (CFIR). It explored partner perspectives (i.e., knowledge producers and users) on factors that supported their engagement in KM activities. The factors generated were subsequently mapped onto the ERIC taxonomy to identify relevant strategies to support partnerships development for KM activities (e.g., shared goals among team members mapped onto the ERIC strategy Build a Coalition). Each factor generated was determined to have a corresponding ERIC strategy to support the operationalization of that factor. CONCLUSIONS: This case example and discussion bolster the utility of existing taxonomies and frameworks to support the development and sustainability of partnerships to support engagement in KM activities, a promising next step for developing strategies to support partnerships. Opportunities for future development are also discussed, including identifying other theories, models, and frameworks that may contribute to a comprehensive suite of empirically informed partnership strategies, as well as the necessity to make strategies and approaches available to non-specialist audiences.
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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 | 0.117 | 0.197 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.011 | 0.028 |
| Scholarly communication | 0.030 | 0.059 |
| Open science | 0.008 | 0.018 |
| Research integrity | 0.021 | 0.019 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".