Knowledge translation: Principles and practicalities for trainees within interdisciplinary health research teams
Bibliographic record
Abstract
Within a dynamic health research environment with trends toward increasing accountability, governments and funding agencies have placed increased emphasis on knowledge translation (KT) as a way to optimize the impact of research investments on health outcomes, research products and health service delivery. As a result, there is an increasing need for familiarity with the principles of KT frameworks and components of KT strategies. Accordingly, health research trainees (graduate students and post-doctoral fellows) must be supported to enhance their capacity to understand KT principles and the practicalities of implementing effective KT practices.In this paper, the unique opportunities and challenges that trainees within an interdisciplinary research team encounter when they begin to understand and apply constructive and relevant KT practices are considered. Our commentary is based on trainee experiences within the Preterm Birth and Healthy Outcomes Team (PreHOT), an interdisciplinary research team.
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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 | 0.238 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.075 |
| Scholarly communication | 0.024 | 0.021 |
| Open science | 0.006 | 0.032 |
| Research integrity | 0.023 | 0.025 |
| Insufficient payload (model declined to judge) | 0.003 | 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".