From Promise to Practice: How Health Researchers Understand and Promote Transdisciplinary Collaboration
Bibliographic record
Abstract
There is an increasing emphasis on transdisciplinary research to address the complex challenges faced by health systems. However, research has not adequately explored how members of transdisciplinary research teams perceive, understand, and promote transdisciplinary collaboration. As such, there is a need to investigate collaborative behaviors, knowledge, and the impacts of transdisciplinary research. To address this gap, we conducted a longitudinal realist evaluation of transdisciplinary collaboration within a 5-year National Health and Medical Research Council-funded Center of Research Excellence in Transdisciplinary Frailty Research. The current study aimed to explore researchers' perceptions and promotion of transdisciplinary research specifically within the context of frailty research using qualitative methods. Participants described transdisciplinary research as a collaborative and integrative approach that involves individuals from various disciplines working together to tackle complex research problems. However, participants often used terms like interdisciplinary and multidisciplinary interchangeably, indicating that a shared understanding of transdisciplinary research is needed. Barriers to transdisciplinary collaboration included time constraints, geographical distance, and entrenched collaboration patterns. To overcome these challenges, participants suggested implementing strategies such as creating a shared vision and goals, establishing appropriate collaboration systems and structures, and role modeling collaborative behaviors, values, and attitudes. Our findings underscore the need for practical knowledge in developing transdisciplinary collaboration and leadership skills across different career stages. In the absence of formal training, sustained and immersive programs that connect researchers with peers, educators, and role models from various disciplines and provide experiential learning opportunities, may be valuable in fostering successful transdisciplinary collaboration.
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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 | 0.349 | 0.403 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.033 | 0.079 |
| Scholarly communication | 0.067 | 0.075 |
| Open science | 0.013 | 0.083 |
| Research integrity | 0.027 | 0.038 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".