Virtual Interprofessional Education
Bibliographic record
Abstract
PURPOSE OF STUDY: This study assessed the effectiveness of a virtual interprofessional education (IPE) discharge planning simulation, focusing on collaborative patient education, and recommendations for hospital discharge. PRIMARY PRACTICE SETTING: An acute care hospital. METHODOLOGY AND SAMPLE: The study utilized a virtual IPE discharge planning simulation for health care students from six different programs. The simulation involved prebriefing, icebreaker, team meeting, patient interaction, and debriefing. Assessment included pre- and post-IPE surveys that included the Interprofessional Education Collaborative (IPEC) Competency Self-Assessment Tool, and video analysis using the Modified McMaster-Ottawa Rating Scale. RESULTS: Student participants from diverse health care programs ( n =143) included nursing ( n = 20), occupational therapy ( n = 21), physical therapy ( n = 42), physician assistant ( n = 38), respiratory therapy ( n = 3), and social work ( n = 19). All programs except respiratory therapy showed significant improvement in IPEC Competency scores post-IPE, with positive outcomes for understanding other professions' roles. Students' self-reported perceptions of team performance were rated highly in various categories. Video analysis demonstrated strong interrater reliability for team scores. IMPLICATIONS FOR CASE MANAGEMENT PRACTICE: Effective hospital discharge planning is vital for cost reduction and patient care improvement. IPE emphasizes collaborative learning among health care students. Previous studies highlight positive outcomes from IPE discharge planning, including virtual formats. This virtual IPE discharge planning simulation significantly improved students' understanding and collaboration competencies, evident in increased IPEC scores across five professions.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".