Integrated knowledge translation (IKT) in preclinical research: a scoping review protocol
Bibliographic record
Abstract
Integrated knowledge translation (iKT) is a collaborative research approach that emphasizes the meaningful and active participation of knowledge users throughout the research process. Evidence suggests that iKT has the potential to increase the relevance, applicability, and use of research findings. As such, iKT has been increasingly utilized in health research in recent years. However, the extent to which iKT has been applied in preclinical research and its effectiveness are unknown. This review will be conducted as part of the Improving Neuroplasticity through Spaced Prefrontal intermittent-Theta-Beta-Stimulation REfinement in Depression (INSPiRE-D) project, which features preclinical research from mouse models to human work. The aim of this review is to scope what is currently known in the peer-reviewed literature about the use of iKT in preclinical research. Guided by this overarching aim, this scoping review has three specific objectives: 1) To map the current peer-reviewed literature on the use of iKT within preclinical research contexts 2) To identify benefits and challenges to employing iKT approaches in preclinical research documented in the peer-reviewed literature 3) To inform the development of a model of bench-to-bedside collaboration aiming to close the gap between preclinical and clinical research and beyond.
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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 | 0.205 | 0.185 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.025 | 0.021 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.059 | 0.016 |
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".