The Role of Interprofessional Education in Training Healthcare Providers for Integrated Healthcare: A Scoping Review
Bibliographic record
Abstract
Background: Longer lifespans and living with multiple chronic conditions are driving necessary change in healthcare systems. There is an increasing shift towards team-based integrated care, to provide person-centred care that is accessible, continuous, and of high quality. Health professional roles are changing rapidly; traditional educational approaches no longer suffice. New models of care require new models of learning – from a focus on workforce planning for professionals to workforce planning for patients and populations. The World Health Organization and the Institute of Medicine acknowledge that preparation of the healthcare workforce has not kept pace with these changes. Interprofessional education (IPE) and professional development training that includes partnering with patients, providers, and communities are identified as key solutions. However, understanding how IPE supports workforce development for integrated care remains unclear. This scoping review aimed to answer the question: What is the role of IPE in training healthcare professionals to work in integrated care? The focus of this review was on post-licensure health care professionals (HCPs) in the current workforce versus preparation of students in academic settings.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".