How transdisciplinary research teams learn to do knowledge translation (KT), and how KT in turn impacts transdisciplinary research: a realist evaluation and longitudinal case study
Bibliographic record
Abstract
BACKGROUND: Transdisciplinary research and knowledge translation are increasingly regarded as key concepts underpinning applied research across the health and social sciences, due to their presumed potential in addressing complex, "wicked" problems and improving the use of research in practice and policy, respectively. Despite sharing an impact mandate, the relationship between transdisciplinary research collaboration and knowledge translation remains unclear. In response, we examined the relationship between transdisciplinary collaboration and knowledge translation to generate these understandings with a view towards maximizing the impact of collaborative efforts. METHODS: We undertook a realist evaluation and longitudinal case study of a 5-year National Health and Medical Research Council-funded Centre of Research Excellence in Transdisciplinary Frailty Research. Data were collected between February 2017 and March 2020 over three rounds of theory development, refinement and testing using interviews, observation, document review and visual elicitation as data sources. The Human Research Ethics Committee of the University of Adelaide approved this study. RESULTS: Iterative analysis of narrative interviews and visual data led to the development of three overarching programme theories explicating the reciprocal relationship between KT understandings and transdisciplinary team process. These programme theories revolve around the concept of a network, which we define in alignment with extant theoretical literature on network mechanisms and complex networks as graphically representable networks of agents/people (nodes) joined by social relationships (links). Our findings demonstrate that under the right contextual conditions, transdisciplinary team members respond through an improved ability to (1) navigate the network, (2) negotiate the network and (3) mobilize the network. CONCLUSIONS: This research demonstrates the reciprocity and mutually supportive relationship between transdisciplinary research and knowledge translation. Our findings suggest that embedding a collaborative knowledge translation framework and providing resources such as facilitation and distributed leadership within a transdisciplinary team can improve collaboration and support transdisciplinary research objectives.
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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 | 0.187 | 0.201 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| 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, 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".