Using a Research-Informed Interprofessional Curriculum Framework to Guide Reflection and Future Planning of Interprofessional Education in a Multi-Site Context
Bibliographic record
Abstract
Background: Over the past two years health educators in Australia have benefited from funding made available from national organizations such as the Office of Learning and Teaching (OLT) and Health Workforce Australia (HWA). Funded research has been conducted into educational activities across the country that aim to promote integrated and sustainable interprofessional learning.Methods and Findings: A collaboration between multiple stakeholders led to theestablishment of a consortium of nine universities and interprofessional organizations. This collaboration resulted in a series of research studies and the development of a conceptual framework to guide the planning and review of interprofessional health curricula. A case study of the development of a suite of health education programs at a regional university in Australia is used to demonstrate how the framework can be used to guide curricular reflection and to plan for the future. Shedding a light on interprofessional health education activities across multiple sites provides a rich picture of current practices and future trends. Commonalities, gaps, and challenges become much more obvious and allow for the development of shared opportunities and solutions.Conclusions: The production of a shared conceptual framework to facilitate interprofessional curriculum development provides valuable strategies for curricular reflection, review, and forward planning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.114 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.009 | 0.018 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".