Development of accreditation standards for interprofessional education: a Canadian Case Study
Bibliographic record
Abstract
Background: Academic institutions worldwide are embedding interprofessional education (IPE) into their health/social services education programs in response to global evidence that this leads to interprofessional collaborative practice (IPC). The World Health Organization (WHO) is holding its 193 member countries accountable for Indicator 3–06 (‘IPE Accreditation’) through its National Health Workforce Accounts. Despite the major influence of accreditation on the quality of health and social services education programs, little has been written about accreditation of IPE. Case study: Canada has been a global leader in IPE Accreditation. The Accreditation of Interprofessional Health Education (AIPHE) projects (2007–2011) involved a collaborative of eight Canadian organizations that accredit pre-licensure education for six health/social services professions. The AIPHE vision was for learners to develop the necessary knowledge, skills and attitudes to provide IPC through IPE. The aim of this paper is to share the Canadian Case Study including policy context, supporting theories, preconditions, logic model and evaluation findings to achieve the primary project deliverable, increased awareness of the need to embed IPE language into the accreditation standards for health and social services academic programs. Future research implications are also discussed. Conclusions: As a result of AIPHE, Canada is the only country in the world in which, for over a decade, a collective of participating health/social services accrediting organizations have been looking for evidence of IPE in the programs they accredit. This puts Canada in the unique position to now examine the downstream impacts of IPE accreditation.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.037 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".