A Community of Practice for Knowledge Translation Trainees: An Innovative Approach for Learning and Collaboration
Bibliographic record
Abstract
A growing number of researchers and trainees identify knowledge translation (KT) as their field of study or practice. Yet, KT educational and professional development opportunities and established KT networks remain relatively uncommon, making it challenging for trainees to develop the necessary skills, networks, and collaborations to optimally work in this area. The Knowledge Translation Trainee Collaborative is a trainee-initiated and trainee-led community of practice established by junior knowledge translation researchers and practitioners to: examine the diversity of knowledge translation research and practice, build networks with other knowledge translation trainees, and advance the field through knowledge generation activities. In this article, we describe how the collaborative serves as an innovative community of practice for continuing education and professional development in knowledge translation and present a logic model that provides a framework for designing an evaluation of its impact as a community of practice. The expectation is that formal and informal networking will lead to knowledge sharing and knowledge generation opportunities that improve individual members' competencies (eg, combination of skills, abilities, and knowledge) in knowledge translation research and practice and contribute to the development and advancement of the knowledge translation field.
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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 | 0.036 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.018 | 0.015 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.006 | 0.033 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".