Key elements for interprofessional education. Part 1: The learner, the educator and the learning context
Bibliographic record
Abstract
This paper is the first of two that highlights key elements needed for consideration in the planning and implementation of interprofessional educational (IPE) interventions at both the pre and post-licensure qualification education levels. There is still much to be learned about the pedagogical constructs related to IPE. Part 1 of this series discusses the learning context for IPE and considers questions related to the "who, what, where, when and how" related to IPE. Through a systematic literature review that was conducted for Health Canada in its move to advance Interprofessional Education for Patient Centred Practice (IECPCP), this paper provides background information that can be helpful for those involved in an interprofessional initiative. A historical review of IPE sets the international context for this area and reflects the work that has been done and is currently being initiated and implemented to advance IPE for health professional students. Much can be learned from the literature related to the pedagogical approaches that have been tried and the issues that need to be addressed related to the learner, the educator and the learning context which this paper examines.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".