A Middle Eastern journey of integrating Interprofessional Education into the healthcare curriculum: a SWOC analysis
Bibliographic record
Abstract
BACKGROUND: Interprofessional education (IPE) is an emerging concept in the Middle East with a number of health professional degree programs continually striving to meet international accreditation requirements to enhance the quality of education and ensure high standards are maintained. Using the College of Pharmacy at Qatar University (CPH QU) as a model, this article describes the IPE initiatives coordinated through the College's IPE Committee, with representation from fourteen programs at four Healthcare institutions: Qatar University; Weill Cornell Medical College in Qatar; the University of Calgary in Qatar; and the College of North Atlantic in Qatar. These activities are based on the model proposed by the University of British Columbia across the different pharmacy professional years. Learning objectives for these initiatives were selected from the IPE shared competency domains and competency statements developed for Qatar context. METHOD: A meeting with six faculty members, who have been instrumental to designing and executing the IPE activities in the previous 2 years, was convened. Faculty members reflected on IPE activities and collaborations with other participating programs. A structured SWOC (Strengths, Weaknesses, Opportunities, Challenges) framework was used to guide discussion. The discussion was recorded and notes were taken during the meeting. Raised points were categorized into each SWOC category for the final analysis. RESULTS: Implementation of IPE program is a major undertaking with a number of challenges that require invested time to overcome. This article highlights the importance of incorporating IPE into healthcare curricula to graduate students ready for collaborative practice in the workforce. Learning objectives for IPE initiatives need to be based on shared competency domains. When developing and implementing an IPE program it is necessary to align activities under a strong theoretical framework. This should be done under the leadership of an IPE steering group or committee to oversee the integration of IPE into the healthcare curriculum. CONCLUSION: The article presents many lessons learned through IPE implementation that are relevant to other academic institutions keen to incorporate IPE into their programs and also provides a successful model for integrating IPE into healthcare 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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".