Learning together to teach together: Interprofessional education and faculty development
Bibliographic record
Abstract
Interprofessional education for collaborative patient-centered practice has been identified as a key mechanism to address health care needs and priorities. Faculty development can play a unique role in promoting interprofessional education (IPE) by addressing some of the barriers to teaching and learning that exist at both the individual and the organizational level, and by providing individuals with the knowledge and skills needed to design and facilitate IPE. This article highlights a number of approaches and strategies that can facilitate IPE. In particular, it is recommended that faculty development initiatives aim to bring about change at the individual and the organizational level; target diverse stakeholders; address three main content areas, notably interprofessional education and collaborative patient-centred practice, teaching and learning, and leadership and organizational change; take place in a variety of settings, using diverse formats and educational strategies; model the principles and premises of interprofessional education and collaborative practice; incorporate principles of effective educational design; and consider the adoption of a dissemination model to implementation. Clearly, faculty members play a critical role in the teaching and learning of IPE and they must be prepared to meet this challenge.
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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 | 0.029 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".