Defining the Scope of Knowledge Translation Within a National, Patient-Oriented Kidney Research Network
Bibliographic record
Abstract
PURPOSE OF PROGRAM: Integrated knowledge translation (IKT) is a collaborative approach whereby knowledge created through health research is utilized in ways that are relevant to the needs of all stakeholders. However, research teams have limited capacity and know-how for achieving IKT, resulting in a disconnect between the generation and application of knowledge. The goal of this report is to describe how IKT research was achieved across a large-scale, patient-oriented research network, Canadians Seeking Solutions and Innovations to Overcome Chronic Kidney Disease (Can-SOLVE CKD). SOURCES OF INFORMATION: Resources to facilitate knowledge translation (KT) planning across the network were developed by the Can-SOLVE CKD Knowledge User/Knowledge Translation Committee with reference to established Canadian KT and patient engagement tools and frameworks, review of the published and gray literature, and expertise of committee members. METHODS: The Can-SOLVE CKD Knowledge User/Knowledge Translation Committee consisting of patient partners, health care providers, policymakers, and researchers provided oversight of the development and implementation of the network's IKT initiatives. Guided by its strategic framework, the committee developed KT planning templates and review checklists to assist network projects with preparing for dissemination, implementation, and scale and spread of their interventions. The committee has acted in a consultative capacity to facilitate IKT across network initiatives and has supported capacity building through KT activities aimed at network membership and knowledge users more broadly. KEY FINDINGS: The Can-SOLVE CKD Knowledge User/Knowledge Translation Committee established a nation-wide strategy for KT infrastructure and capacity building. Acting as a knowledge intermediary, the committee has connected research teams with knowledge users across Canada to support practices and policies informed by evidence generated by the network. The committee has developed KT initiatives, including a Community of Practice, whereby participants across different regions and disciplines convene regularly to share health research knowledge and communications strategies relevant to the network. Critically, patients are engaged and contribute throughout the research process. Examples of IKT activities from select projects are provided, as well as ways for sustaining the network's KT platform. LIMITATIONS: The KT resources developed by the committee were adapted from other established resources to meet the needs of the network and have not undergone formal evaluation in this context. Given the broad scope of the network, resources to facilitate implementation and knowledge user engagement may not meet the needs of all initiatives and must be tailored accordingly. Knowledge barriers, including a lack of information and skills related to conceptual and practical aspects of KT, among network members provided a rationale for various KT capacity-building initiatives. IMPLICATIONS: The approach described here offers a practical method for achieving IKT, including how to plan, implement, and sustain initiatives across large-scale health research networks. Within the context of Can-SOLVE CKD, these efforts will shorten knowledge-practice gaps through producing and applying relevant research to improve the lives of people living with kidney disease.
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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 | 0.385 | 0.268 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.029 | 0.039 |
| Scholarly communication | 0.035 | 0.020 |
| Open science | 0.008 | 0.034 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".