A Case Study of Organizational and Curricular Attributes for Interprofessional Education: A Model for Sustainable Curriculum Delivery
Bibliographic record
Abstract
Background: In health and social care (HASC) professional education, interprofessional competencies are optimally developed by engaging in interprofessional education (IPE) activities that are delivered sustainably along a continuum. Ultimately, active engagement in IPE is meant to prepare future practitioners for interprofessional collaborative practice (IPCP), which leads to improved patient/client and community-oriented outcomes. Methods and Findings: This qualitative case study explores how four Canadian post-secondary institutions deliver IPE within their HASC professional education programmatic structures. Data were collected from institutional websites, publicly available IPE relevant records and documents, and interviews with coordinators and faculty/facilitators of IPE curriculum. Data were inductively analyzed to generate relevant themes, followed by a deductive analysis guided by the five accreditation standards domains identified in the Accreditation of Interprofessional Health Education (AIPHE) projects. Analyses of the data resulted in five attributes: 1) central administrative unit, 2) longitudinal and integrative program, 3) theoretically informed curriculum design, 4) student-centred pedagogy, and 5) patient/client-oriented approach. Conclusions: Using these attributes and guided by AIPHE’s accreditation standards domains, an organizational-curricular model for sustainable IPE is proposed, through which we assert that IPE reinforced through these organizational and curricular supports reflects successful programming, leading to patient/client-oriented outcomes.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".