How does integrated knowledge translation (IKT) compare to other collaborative research approaches to generating and translating knowledge? Learning from experts in the field
Bibliographic record
Abstract
BACKGROUND: Research funders in Canada and abroad have made substantial investments in supporting collaborative research approaches to generating and translating knowledge as it is believed to increase knowledge use. Canadian health research funders have advocated for the use of integrated knowledge translation (IKT) in health research, however, there is limited research around how IKT compares to other collaborative research approaches. Our objective was to better understand how IKT compares with engaged scholarship, Mode 2 research, co-production and participatory research by identifying the differences and similarities among them in order to provide conceptual clarity and reduce researcher and knowledge user confusion about these common approaches. METHODS: We employed a qualitative descriptive method using interview data to better understand experts' perspectives and experiences on collaborative research approaches. Participants' responses were analysed through thematic analysis to elicit core themes. The analysis was centred around the concept of IKT, as it is the most recent approach; IKT was then compared and contrasted with engaged scholarship, Mode 2 research, co-production and participatory research. As this was an iterative process, data triangulation and member-checking were conducted with participants to ensure accuracy of the emergent themes and analysis process. RESULTS: Differences were noted in the orientation (i.e. original purpose), historical roots (i.e. disciplinary origin) and partnership/engagement (i.e. role of partners etc.). Similarities among the approaches included (1) true partnerships rather than simple engagement, (2) focus on essential components and processes rather than labels, (3) collaborative research orientations rather than research methods, (4) core values and principles, and (5) extensive time and financial investment. Core values and principles among the approaches included co-creation, reciprocity, trust, fostering relationships, respect, co-learning, active participation, and shared decision-making in the generation and application of knowledge. All approaches require extensive time and financial investment to develop and maintain true partnerships. CONCLUSIONS: This qualitative study is the first to systematically synthesise experts' perspectives and experiences in a comparison of collaborative research approaches. This work contributes to developing a shared understanding of collaborative research approaches to facilitate conceptual clarity in use, reporting, indexing and communication among researchers, trainees, knowledge users and stakeholders to advance IKT and implementation science.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
| gpt | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.243 | 0.308 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.012 | 0.035 |
| Scholarly communication | 0.038 | 0.038 |
| Open science | 0.005 | 0.032 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".