Barriers and Enablers to Transciplinarity in Practice
Bibliographic record
Abstract
Transdisciplinary (TD) knowledge integration is required to tackle complex societal challenges, such as shaping the future of work for nursing care in the face of workforce shortages. However, moving from theoretical considerations on what makes TD work to real-world practice is hard and often case-specific, leaving little room for actionable methodological guidelines. The aim of this paper is to disseminate TD content and process learnings from a 6-month pilot project at a Dutch academic hospital. The project was commissioned by a senior human-robot interaction researcher in December 2022 after he presented a vision of transdisciplinary research integration to shape the future of work. This vision translated into an approach where roboticists, designers, psychologists, organisational scholars, and nurses strived to integrate academic, professional, and experiential knowledge. As a result, the core project activities were performed by a team of four junior researchers representing four out of five of these disciplines in collaboration with eight practising nurses. We particularly focus on the second half of the project, where, over the course of three months, the core project team engaged in a four-stage TD research process: Grounding in literature and research site Understanding current nursing work processes Joint exploration of preferable and plausible future work processes supported by robotics (TD workshop) Sensemaking and joint reflection This paper aims to capture our learnings about content—the lived experience of oncology nurses and potential avenues for change on the work floor from an organisational, interaction design, worker and robotics perspective—and about the process—barriers and enablers of transdisciplinary practices as reflected upon by the authors. We have come to understand this project as a meeting of two hierarchical systems of knowledge production: a TD System (an academic and innovation consortium of which the authors of this paper are part) and a Convergence System (representing the commissioning organisation: academic hospital aimed at accelerating technological innovation in health). Both systems contain academic- and non-academic actors with specific knowledge, expertise, experiences, interests, and power dynamics, providing for rich learnings and challenges within and across (sub)systems. We report on the project genesis, what content was learned from the four-phase methodology, and most importantly—what we learned about the process that can be taken into subsequent TD projects that aim to understand and shape the future of work with and for workers.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".