Co-Creation of Knowledge Mobilization Strategies: Findings from a Collaborative Symposium
Bibliographic record
Abstract
Context: Effectively mobilizing the uptake of research findings requires useful strategies as well as building capacity in primary health care (PHC) research communities for these activities. In Canada, a unique program exists - Transdisciplinary Understanding and Training on Research-Primary Heath Care (TUTOR-PHC) that develops capacity for interdisciplinary PHC research. To celebrate the 20th anniversary of TUTOR-PHC, we held a Knowledge Mobilization Symposium. Objective: To describe the results of a Symposium to co-create capacity for interdisciplinary PHC research knowledge mobilization. Study Design and Analysis: The Symposium included keynote speakers, poster sessions, and two forums with small group activities and discussion. Forum 1 focused on how to effectively and actively mobilize the uptake of research findings into policy and practice. Forum 2 focused on identifying key components of accessible research syntheses in graphic form and impact narratives. A thematic analysis of the summaries from the forums was conducted. Setting or Dataset: Canada Population Studied: Sixty-three participants from all TUTOR-PHC cohorts as well as mentors, patientpartners, and knowledge users. Intervention/Instrument: N/A Outcome Measures: N/A Results: Symposium participants came from across Canada, Australia, New Zealand, UK, France, and India. Forum 1 focused on effective uptake of research findings - themes common across small discussion groups included: the importance of creating meaningful engagement at the outset of the research with key partners, building sustainable long-term relationships based on trust and humility, and creating a safe space for everyone (researchers, patient partners and policy-makers) to have an equal voice. Forum 2 focused on identifying important components of research syntheses in graphic form and impact narratives. All discussion groups noted the challenges in developing creative and engaging syntheses and narratives. A range of strategies from bar graphs to video games, billboards and podcasts were identified. While infographics were recognized as a common strategy, it was important to tailor them to the intended target audience. Conclusions: Evaluation results demonstrate that the Symposium achieved its objectives. Knowledge mobilization is a key component of the research process that requires careful thought and planning.
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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 | 0.066 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.028 | 0.013 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.005 | 0.030 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".