Integrating an interprofessional education initiative: Evidence from King Abdulaziz University
Bibliographic record
Abstract
PURPOSE: This paper examines current issues with interprofessional education (IPE) at King Abdulaziz University (KAU) and discusses initiatives for integrating IPE into the medical curricula at KAU. METHODS: We reviewed the current body of literature, studied reports from IPE conferences and workshops organized at KAU, and synthesized participants' feedback from the IPE programs, including an online survey. RESULTS: A total of 506 participants responded to the online survey. Respondents rated Interprofessional Collaborative Learning as the highest category of IPE, followed by Interprofessional Self-Improvement and Interprofessional Relationship. A hybrid conceptual framework is proposed, to tackle the issue of role clarification across all healthcare colleges at KAU. This proposition was found to be necessary due to the current state of the undergraduate curriculum which does not prepare students properly for professional collaboration. CONCLUSIONS: The hybrid model may narrow the gap in IPE by emphasizing professional identity while reducing autonomy. Recommendations toward IPE are presented. Challenges toward IPE reform are discussed in the context of implementation at KAU and at other medical schools in the region.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".