Situating Interprofessional Education Curriculum within a Theoretical Framework for Productive Engaged Learning: Integrating Epistemology, Theory, and Competencies
Bibliographic record
Abstract
Interprofessional education (IPE) has a longstanding presence in the health and social care (HASC) professions, by which its sustainable implementation in HASC professional education has the potential to effectively prepare HASC professional students for interprofessional collaborative practice (IPCP). Implementation of IPE has increased over the last two decades with the emergence of a curriculum guided by constructivist epistemology and learning theories that emphasize demonstrating competence in practice. Nonetheless, since IPE first emerged in the early 1960s, most IPE initiatives have been sporadic and lacked guidance through theoretical underpinnings. This conceptual article first discusses why it is important to have theory drive HASC professional education. Next, it explores what is meant by curriculum, followed by a discussion on the importance of curriculum theory to HASC professional education processes. This article then illustrates the learning theories arising from behaviourist and constructivist epistemologies that inform curriculum theory in the HASC professions, with particular emphasis on how constructivist learning theories inform IPE. Lastly, the article proposes a theoretical framework for productive engaged learning through which IPE opportunities may be grounded, leading to student proficiency in interprofessional professional competencies (knowledge, skills, and dispositions), establishment of professional communities of practice, and eventual improvement of patient/client-oriented outcomes.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".