Barriers and Enablers to Transciplinarity in Practice
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,012 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,004 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».