Key Strategies for First-Time Interprofessional Teachers and those Developing New Interprofessional Education Programs
Bibliographic record
Abstract
Background: Evidence that interprofessional education (IPE) leads to better teamwork and improved interprofessional collaboration has created a drive to establish pre-registration IPE health science and social care programs. Yet there is limited guidance available for teachers new to IPE.Objectives: To provide first-time teachers practical strategies to undertake IPE.Methods: Strategies developed from experience.Findings: First-time IPE teachers should: try to join an existing IPE team; observe and collaborate with experienced IPE teachers; contribute to the development of new IPE programs; seek institutional support; undertake IPE evaluation and research; and gain high-level institutional endorsement.Conclusions: Six strategies are designed to overcome commonly recognized problemsand enable first-time teachers to more confidently develop or engage in IPE,thus supporting students to attain skills in interprofessional collaboration.
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".