Virtual Interprofessional Education (VIPE)–The VIPE Program: VIPE Security, a Multi-sectoral Approach to Dealing with Complex Wicked Problems
Bibliographic record
Abstract
Introduction: The Virtual Interprofessional Education program is a multi-institutional consortium collaborative formed between five universities across the United States. As of January 2022, the collaborative includes over 60 universities in 30 countries. The consortium brings healthcare students together for a short-term immersive team experience that mimics the healthcare setting. The VIPE program has hosted over 5,000 students in healthcare training programs. The VIPE program expanded to a VIPE Security model to host students across multiple disciplines outside the field of healthcare to create a transdisciplinary approach to managing complex wicked problems. Method: Students receive asynchronous materials ahead of a synchronous virtual experience. VIPE uses the Interprofessional Education Competencies (IPEC) competencies (IPEC, 2016) and aligns with The Health Professions Accreditors Collaborative (HPAC) 2019 guidelines. VIPE uses an active teaching strategy, problem or case-based learning (PBL/CBL), which emphasizes creating an environment of psychological safety and its antecedents (Frazier et al., 2017 and Salas, 2019, Wiss, 2020). Following this model, VIPE Security explores whether the VIPE model can be tailored to work across multiple sectors to discuss management of complex wicked problems to include: climate change, disaster, cyber attacks, terrorism, pandemics, conflict, forced migration, food/water insecurity, human/narco trafficking etc. VIPE Security has hosted two events to include professionals in the health and security sectors to work through complex wicked problems to further understand their roles, ethical and responsible information sharing, and policy implications. Results: VIPE demonstrates statistically significant gains in knowledge towards interprofessional collaborative practice as a result of participation. VIPE Security results are currently being analyzed. Conclusion: This transdisciplinary approach to IPE allows for an all-hands-on-deck approach to security, fostering early education and communication of students across multiple sectors. The VIPE Security model has future implications to be utilized within multidisciplinary organizations for practitioners, governmental agencies, and the military.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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 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".