Interprofessional learning in practice-based settings: AMEE Guide No. 169
Bibliographic record
Abstract
The provision of optimum health care services requires collaboration of health care professionals in integrated interprofessional (IP) teams. This guide addresses the practical aspects of establishing and delivering pre-licensure IP programs to prepare graduates of health professional programs to work in teams and wider collaboration, and consequently enhance the quality of health care. The main updated IP frameworks are presented to highlight commonalities that represent the essential competencies and outcomes of programs implementing interprofessional education (IPE). We discuss how these may be adapted to the local context, and present examples of models of implementation to guide the initial steps of establishing similar programs. Examples of pre-licensure IP practice-based learning, such as community-based, simulation-based, student-run and led clinics, and interprofessional training wards, and post-licensure interprofessional learning (IPL), are described. We consider assessment of IPL along the continuum of learning IP. This guide also emphasises the need to tailor faculty development programs for local contexts and consider factors affecting sustainability such as funding and accreditation. We finish with the governance of IP programs and how global IP networks may support interprofessional practice-based learning from development to delivery.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.053 | 0.044 |
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".