Interprofessional Competency Frameworks in Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<ns4:p>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.</ns4:p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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 it