A Living Educational Theory of Knowledge Translation: Improving Practice, Influencing Learners, and Contributing to the Professional Knowledge Base
Bibliographic record
Abstract
This paper captured our joint journey to create a living educational theory of knowledge translation (KT). The failure to translate research knowledge to practice is identified as a significant issue in the nursing profession. Our research story takes a critical view of KT related to the philosophical inconsistency between what is espoused in the knowledge related to the discipline of nursing and what is done in practice. Our inquiry revealed “us” as “living contradictions” as our practice was not aligned with our values. In this study, we specifically explored our unique personal KT process in order to understand the many challenges and barriers to KT we encountered in our professional practice as nurse educators. Our unique collaborative action research approach involved cycles of action, reflection, and revision which used our values as standards of judgment in an effort to practice authentically. Our data analysis revealed key elements of collaborative reflective dialogue that evoke multiple ways of knowing, inspire authenticity, and improve learning as the basis of improving practice related to KT. We validated our findings through personal and social validation procedures. Our contribution to a culture of inquiry allowed for co-construction of knowledge to reframe our understanding of KT as a holistic, active process which reflects the essence of who we are and what we do.
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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 | 0.029 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.037 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".