Educational Design Research on Person-Centred, Interprofessional Education for Collaborative Practice (IPECP)
Bibliographic record
Abstract
Within the rehabilitation sector, students and professionals collaborate interprofessionally when focusing on the functioning of patients. Developing and implementing person-centred, interprofessional education in an institution requires coordinated implementation actions across multiple divisions. The objectives of this study were twofold: firstly, to explore the work processes of educators who implement person-centred, interprofessional education for collaborative practice (IPECB) which, secondly, served to develop process guide materials for educators from various settings. Educational Design Research was conducted in the Erasmus+ project INPRO from 2021 to 2023. It followed an iterative, process-oriented approach that consisted of four complementary workstreams: 1) Literature, needs, and collection of approaches; 2) IPECP design thinking and piloting with project stakeholders; 3) Process guide development and usability testing; 4) Exchange and refinement. A synthesis from each workstream’s findings served to explore the content and structure of process guide materials. Three design topics emerged: a) ‘Facilitating Interprofessional Education in a Global Classroom Setting’; b) ‘Interlinking Higher Education and Rehabilitation Practice’; c) ‘Facilitating Interprofessional Collaborative Practice in Rehabilitation’. This developed theory shows that the needs of educators differed between higher education and rehabilitation settings. As a result, the process guide materials consist of context-specific content. The third topic showed links between the settings regarding the educators’ work process. These findings determined the structure of the process guides. Interlinkages bear a potential for facilitating the transition of educators and students from theory to practice. Future studies may explore the applicability of the findings to other settings and to collaborative (online) learning in general.
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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 | 0.079 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".