Exploring the Efficacy of a Virtual First Year Interprofessional Education Event
Bibliographic record
Abstract
Interprofessional education (IPE) activities are utilized in health education programs to develop interprofessional collaboration (IPC) competencies. All first-year healthcare students at three postsecondary learning institutions attend a mandatory introductory IPE event annually. During the 2020/2021 academic year, the event was moved from a face-to-face activity to a virtual format due to the COVID-19 pandemic restrictions. This study examined whether the virtual IPE activity was effective in supporting the development of interprofessional competencies for first-year healthcare students. Two hundred and six students attended a synchronous didactic presentation on IPE competencies and discussed a simulated case in interprofessional groups of eight students and two faculty facilitators. The Interprofessional Collaborative Competency Attainment Survey (ICCAS) was used to measure the students’ opinions on interprofessional competencies. Paired t-tests were used to compare the pre- and post-scores. One hundred and nine (52.9% response rate) students completed the survey. Surveys from 99 students with matched pre- and post-scores were included in the study. The ICCAS competencies showed improvements (p < 0.05) in all of the students’ self-reported IPE competencies following the activity compared to before the training. Our findings indicate that the virtual IPE activity is effective in facilitating the development of IPC for first-year healthcare students.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".