Theories to aid understanding and implementation of interprofessional education
Bibliographic record
Abstract
Multiple events are calling for greater interprofessional collaboration and communication, including initiatives aimed at enhancing patient safety and preventing medical errors. Education is 1 way to increase collaboration and communication, and is an explicit goal of interprofessional education (IPE). Yet health professionals to date are largely educated in isolation. IPE differs from most traditional continuing education in that knowledge is largely socially created through interactions with others and involves unique collaborative skills and attitudes. It requires thinking differently about what constitutes teaching and learning. The article draws upon a small number of social and learning theories to explain the rationale for IPE needing a new way of thinking, and proposes approaches to guide development and implementation of IP continuing education. Social psychology and complexity theory explain the influence of the dynamism and interaction of internal (cognitive) and external (environmental) factors upon learning and set the stage for IPE. Theories related to professionalism and stereotyping, communities of practice, reflective learning, and transformative learning appear central to IPE and guide specific educational interventions. In sum, IPE requires CE to adopt new content, recognize new knowledge, and use new approaches for learning; we are now in a different place.
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 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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".