Learning theories and their applications in interprofessional education (IPE) to foster dual identity development
Bibliographic record
Abstract
Interprofessional teaching and learning is a key component of interprofessional education for collaborative practice (IPECP), which aims to prepare health professional students and practitioners to work effectively and collaboratively with each other and with patients to address the Quintuple Aim (better health, better care, better value, better work experience, and better health equity). However, interprofessional teaching and learning is complex and challenging, as learners come together with diverse professional knowledge, skills, and experience; potential preconceived notions and prejudices against each other, and diverse expectations and conditions in which learning will occur. As a result of this complexity, there is no one-size-fits-all IPECP intervention as the current literature and practice lack a clear, consistent theoretical foundation, and guidance for interprofessional teaching and learning. This article aims to critically analyze and apply the main four learning theories (behaviorism, cognitivism, constructivism, and humanism) in interprofessional education (IPE). The article discusses the views of learning theories and explains the nature of interprofessional teaching and learning, and the process of designing and implementing interprofessional learning experiences that foster dual (professional and interprofessional) identity in developing future interprofessional practitioners.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.036 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".