The Development of Competencies in Interprofessional Health Care for Use in Health Science Educational Programs
Bibliographic record
Abstract
Background: The Health Education Technology Research Unit (HETRU) at the University of Ontario Institute of Technology (UOIT) has developed an interprofessional framework for use as a learning map to create computer-based simulations that can automatically assess interprofessional competencies of undergraduate health sciences students.Methods: Our interprofessional competency framework was developed through an iterative process of competency mapping. Each iteration involved: 1) a literature review of interprofessional competencies, 2) the mapping of these competencies within a meaningful taxonomy, and 3) the review of the mapping by an expert panel of educators and clinicians.Findings and Conclusions: After three iterations, the research team developed a competency taxonomy that mapped interprofessional competencies from our literature reviews into six competency domains and three cross-cutting themes for each domain. The competency matrix was then used as a learning map to define learning resources related to interprofessional education and learning activities associated with such resources to help students develop competencies in interprofessional healthcare planning and delivery. Interactive, computer-based clinical simulations were then developed to portray opportunities in which the learning resources and activities could be explored and to provide more realistic exposure to complexities in healthcare planning and delivery.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".