Integrated knowledge translation (iKT) in preclinical research: A scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: 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 integrated knowledge translation has the potential to increase the relevance, applicability, and use of research findings. This approach has been increasingly utilized in health research in recent years. However, the extent to which it has been applied in preclinical research and its effectiveness are unknown. To address this gap, we will conduct a scoping review to map the current use, potential benefits, and challenges of iKT in preclinical research. METHODS: Guided by a modified Arksey and O'Malley's scoping review framework, we will systematically search reference lists and key research databases including Medline, Embase, PsycINFO, Cochrane CENTRAL, Cochrane Database of Systematic Reviews, and Web of Science. Peer-reviewed articles written or translated in English that focus on iKT or approaches that align with iKT within the context of preclinical research will be included. 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 (Grant number CAMH File No.22-060). The project's multidisciplinary team and knowledge user advisory committee will be consulted at key points throughout the scoping review process. A person with lived experience co-chairs the project advisory committee, co-authored this manuscript, and will be routinely included in the decision-making process of the scoping review.
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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 | 0.213 | 0.185 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.023 | 0.021 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.072 | 0.020 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".