Bibliographic record
Abstract
Knowledge translation is a dynamic and iterative process that includes the synthesis, dissemination, exchange, and application of knowledge. It is considered the bridge that closes the gap between research and practice. Yet it appears that in all areas of practice, a significant gap remains in translating research knowledge into practical application. Recently, researchers and practitioners in the field of health care have begun to recognize reflection and reflexive exercises as a fundamental component to the knowledge translation process. As a practical tool, reflexivity can go beyond simply looking at what practitioners are doing; when approached in a systematic manner, it has the potential to enable practitioners from a wide variety of backgrounds to identify, understand, and act in relation to the personal, professional, and political challenges they face in practice. This article focuses on how reflexive practice as a methodological tool can provide researchers and practitioners with new insights and increased self-awareness, as they are able to critically examine the nature of their work and acknowledge biases, which may affect the knowledge translation process. Through the use of structured journal entries, the nature of the relationship between reflexivity and knowledge translation was examined, specifically exploring if reflexivity can improve the knowledge translation process, leading to increased utilization and application of research findings into everyday practice.
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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 | 0.203 | 0.364 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.038 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.007 | 0.015 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.029 | 0.014 |
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".