Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. Interest in Interprofessional collaboration (IPC) in health care is increasing, as concerns about patient safety, resource shortages, and effective and efficient care have become explicit priorities. Although there are many exemplars of Interprofessional education (IPE) for collaborative, patient-centered care, there is little in the literature to describe competencies for an Interprofessional collaborative practitioner.Although there are many perspectives on the concept of Interprofessional collaboration, there is scarce literature on the subject related to its application in health education programs. This article describes two Interprofessional competency frameworks that have been developed in Canada and Qatar. These particular frameworks are highlighted because of College of the North Atlantic's (CNA-Q) tie to Canada as a Canadian College operating within Qatar. The frameworks, which have been respectively applied within their own contexts, offer opportunities for the application of Interprofessional competencies elsewhere in the worldwide. The models proposed are reviewed and their utility for educators and practitioners is discussed.The first framework is a Canadian competency framework for IPC that: (1) considers descriptions of collaborative practice and (2) uses existing literature to support a model for describing competencies for collaborative practice. The second framework of Interprofessional health competencies developed in Doha, Qatar originated from a National Priorities Research Project supported by the Qatar National Research Fund. It builds upon a model developed by Qatar University (QU) (El-Awaisiet al., 2017) and the Canadian National Interprofessional Competency Framework for Collaborative Practice (Johnson, et al., 2015). It provides guidance for implementation of IPE in pre- and post-licensure 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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".