Improving interprofessional competence in undergraduate students using a novel blended learning approach
Bibliographic record
Abstract
Interprofessional simulation interventions, especially when face-to-face, involve considerable resources and require that all participants convene in a single location at a specific time. Scheduling multiple people across different programs is an important barrier to implementing interprofessional education interventions. This study explored a novel way to overcome the challenges associated with scheduling interprofessional learning experiences through the use of simulations in a virtual environment (Web.Alive™) where learners interact as avatars. In this study, 60 recent graduates from nursing, paramedic, police, and child and youth service programs participated in a 2-day workshop designed to improve interprofessional competencies through a blend of learning environments that included virtual face-to-face experiences, traditional face-to-face experiences and online experiences. Changes in learners' interprofessional competence were assessed through three outcomes: change in interprofessional attitudes pre- to post-workshop, self-perceived changes in interprofessional competence and observer ratings of performance across three clinical simulations. Results from the study indicate that from baseline to post-intervention, there was significant improvement in learners' interprofessional competence across all outcomes, and that the blended learning environment provided an acceptable way to develop these competencies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".