Barriers and enablers that influence sustainable interprofessional education: a literature review
Bibliographic record
Abstract
The effective incorporation of interprofessional education (IPE) within health professional curricula requires the synchronised and systematic collaboration between and within the various stakeholders. Higher education institutions, as primary health education providers, have the capacity to advocate and facilitate this collaboration. However, due to the diversity of stakeholders, facilitating the pedagogical change can be challenging and complex, and brings a degree of uncertainty and resistance. This review, through an analysis of the barriers and enablers investigates the involvement of stakeholders in higher education IPE through three primary stakeholder levels: Government and Professional, Institutional and Individual. A review of eight primary databases using 21 search terms resulted in 40 papers for review. While the barriers to IPE are widely reported within the higher education IPE literature, little is documented about the enablers of IPE. Similarly, the specific identification and importance of enablers for IPE sustainability and the dual nature of some barriers and enablers have not been previously reported. An analysis of the barriers and enablers of IPE across the different stakeholder levels reveals five key "fundamental elements" critical to achieving sustainable IPE in higher education curricula.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".