Transdisciplinary research for impact: protocol for a realist evaluation of the relationship between transdisciplinary research collaboration and knowledge translation
Bibliographic record
Abstract
INTRODUCTION: Transdisciplinary teams are increasingly regarded as integral to conducting effective research. Similarly, knowledge translation is often seen as a solution to improving the relevance and benefits of health research. Yet, whether, how, for whom and under which circumstances transdisciplinary research influences knowledge translation is undertheorised, which limits its potential impact. The proposed research aims to identify the contexts and mechanisms by which transdisciplinary research contributes to developing shared understandings and behaviours of knowledge translation between team members. METHODS AND ANALYSIS: Using a longitudinal case-study design approach to realist evaluation, we outline a study protocol examining whether, how, if and for whom transdisciplinary collaboration can impact knowledge translation understandings and behaviours within a 5-year transdisciplinary Centre of Research Excellence. Data are being collected between February 2017 and December 2020 over four rounds of theory development, refinement and testing using interviews, observation, document review and visual elicitation as data sources. ETHICS AND DISSEMINATION: The Health Research Ethics Committee of the University of Adelaide approved this study. Findings will be communicated with team members at scheduled intervals throughout the study verbally and by means of creative reflective approaches (eg, arts elicitation, journalling). This research will be used to help support optimal team functioning by identifying strategies to support knowledge sharing and communication within and beyond the team to facilitate attainment of research objectives. Academic dissemination will occur through publication and presentations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.209 | 0.298 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.087 | 0.024 |
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".