Evidence to impact: A community knowledge mobilisation evaluation framework
Bibliographic record
Abstract
Many strategies guide knowledge-sharing to enhance uptake of evidence-based programs in practice, though few have been designed specifically for community settings. We highlight the importance of understanding and evaluating knowledge mobilisation in community settings and present a framework for evaluating knowledge mobilisation that captures short-term knowledge use as it relates to community stakeholders’ goals. To examine the utility of this framework, we applied it to the Pan-Canadian knowledge mobilisation activities of Better Beginnings, Better Futures, a community, university and government collaboration to support child development to its full capabilities. Participants included 31 community stakeholders who had attended a Better Beginnings workshop in one of six Canadian provinces and territories. Qualitative phone interviews were conducted to examine the extent to which knowledge mobilisation activities met participants’ learning needs, and how participants had applied the knowledge gained. Findings demonstrate that most participants had used the information, although the ways information was used varied greatly based on the community context. This application of the knowledge mobilisation framework shows it is useful for capturing diverse forms of short-term knowledge use in community settings. Lessons learned through the evaluation were used to refine the framework. The implications of this framework for academic researchers engaged in undertaking and evaluating community knowledge mobilisation are discussed.
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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 | 0.628 | 0.632 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.063 | 0.036 |
| Science and technology studies | 0.014 | 0.018 |
| Scholarly communication | 0.030 | 0.021 |
| Open science | 0.014 | 0.031 |
| Research integrity | 0.014 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 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; 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".