Transdisciplinary Team Science in Health Research, Where Are We?
Bibliographic record
Abstract
Modern medicine and healthcare systems focus on diagnosing, treating, and monitoring diseases in clinical practice. However, contemporary disease burden is driven by chronic diseases, whose determinants occur across multiple levels of influence, from genetics to changes in the natural, built environments to societal conditions and policies. Conventional discipline-specific approaches are useful for the discovery and accumulation of knowledge on single causes of disease entities. Multidisciplinary collaborations can facilitate the identification of the causes of diseases at multiple levels, while interdisciplinary collaboration remains limited to transferring tools from one discipline to another, perhaps creating new disciplines (molecular epidemiology, etc). However, these forms of disciplinary collaboration fall short in capturing the complexity of chronic disease. In addition, these approaches lack sufficient power to generate knowledge that is translatable into implementable solutions, because of their failure to provide a holistic view limited their ability to capture the complexity of real-world problems. Transdisciplinary collaborations gained popularity in health research in the 1990 s, when disciplinary researchers began to develop integrated research frameworks that transcended discipline-specific methods. Using cancer research as an example, this position paper describes the nature of different disciplinary collaborations, reviews transdisciplinary research projects funded by the US National Cancer Institute, discusses frameworks to develop shared mental models in teams and to evaluate transdisciplinary collaboration, highlights the role of team science in successful transdisciplinary health research, and proposes future research to develop the science of team science.
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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 | 0.153 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.016 | 0.058 |
| Scholarly communication | 0.034 | 0.038 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".