Launching first-year health sciences students into collaborative practice: Highlighting institutional enablers and barriers to success
Bibliographic record
Abstract
Developing and sustaining a comprehensive interprofessional education (IPE) curriculum infused throughout health science programmes at large post-secondary institutions requires not only champions within each program but also collaboration across professional programmes and strong support at an institutional level. The purpose of this article is twofold. First, it reports on the development of an interprofessional learning pathway, an institutional curricular model, and the pathway launch, an introductory learning experience within the context of a large post-secondary institution. The interprofessional curricular model provides a framework to connect the IPE that was previously fragmented across faculties and professional programmes into a scaffolded coherent pathway. The launch exposes students to the principles and competencies of collaborative practice. Second, it explores the dual role of enablers and barriers to IPE within the context of one institution's 20-year experience of developing and delivering. In examining the elements that have sustained the institution's IPE programming, it is highlighted how the seemingly positive elements (e.g., IPE champions and strong university support from central administration) have also served as hindrances within the academy potentially threatening the sustainability and institutionalisation of IPE. We anticipate that this curricular model and learning experiences will provide mechanisms to sustain and foster IPE.
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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 | 0.022 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".