Integrated Knowledge Translation Guiding Principles for Conducting and Disseminating Spinal Cord Injury Research in Partnership
Bibliographic record
Abstract
OBJECTIVE: To address a gap between spinal cord injury (SCI) research and practice by rigorously and systematically co-developing integrated knowledge translation (IKT) guiding principles for conducting and disseminating SCI research in partnership with research users. DESIGN: The process was guided by the internationally accepted The Appraisal of Guidelines for REsearch & Evaluation (AGREE) II Instrument for evaluating the development of clinical practice guidelines. SETTING: North American SCI research system (ie, SCI researchers, research users, funders). PARTICIPANTS: The multidisciplinary expert panel (n=17) and end users (n=35) included individuals from a North American partnership of SCI researchers, research users, and funders who have expertise in research partnerships. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Clarity, usefulness, and appropriateness of the principles. RESULTS: Data regarding 125 principles of partnered research were systematically collected from 4 sources (review of reviews, scoping review, interviews, Delphi consensus exercise). A multidisciplinary expert panel held a 2-day meeting to establish consensus, select guiding principles, and draft the guidance. The panel reached 100% consensus on the principles and guidance document. The final document includes a preamble, 8 guiding principles, and a glossary. Survey data showed that the principles and guidance document were perceived by potential end users as clear, useful, and appropriate. CONCLUSIONS: The IKT Guiding Principles represent the first rigorously co-developed, consensus-based guidance to support meaningful SCI research partnerships. The principles are a foundational tool with the potential to improve the relevance and impact of SCI research, mitigate tokenism, and advance the science of IKT.
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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 | 0.470 | 0.418 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.022 | 0.013 |
| Open science | 0.010 | 0.022 |
| Research integrity | 0.016 | 0.023 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".