Interprofessional Education and Collaborative Practice (IPECP) in Post COVID Healthcare Education and Practice Transformation Era
Bibliographic record
Abstract
In the past two years the world has experienced unprecedented devastation, disruption, and death due to the COVID-19 global Pandemic. At the same time, the Pandemic acts as a transformation catalyst that accelerated the implementation and adoption of long overdue changes in healthcare education and practice, including telehealth and virtual learning. Interprofessional collaboration during the pandemic was able to foster healthcare transformation in several ways at the policy and legislative level, such as the fast-tracking of internationally trained professions. The role and use of digital technologies in healthcare education and practice have been extended and solidified by the pandemic. Macro-level policies acknowledging the importance of population health are key for future interprofessional collaboration of stakeholders to address inequalities. Similarly, interprofessional collaboration is key to addressing the proliferation of misinformation. Interprofessional education and collaborative practice (IPECP) can be effectively utilized to combat misinformation by increasing health literacy amongst health professions and the communities they serve. Despite IPECP being an integral component of promoting patient safety, and holistic, quality care, silos continue to exist. Furthermore, implementation of the Quintuple Aim (better health, better care, better value, better work experience, and better health equity), particularly through the lens of equity, remains elusive. Going forward, the integration and sustainability of IPECP are crucial and the experience of IPECP within the context of the COVID-19 pandemic should be reflected on, researched, and evaluated to inform future global healthcare systems and the workforce to provide and achieve the Quintuple Aim; the goal of all in healthcare. As we are emerging out of the Pandemic, we have a unique opportunity to leverage on the lessons learned from the pandemic in fostering the healthcare transformation through innovation and IPECP. To capitalize on this opportunity and in a collaborative effort, the InterprofessionalResearch.Global (IPR.Global), the American Interprofessional Health Collaborative (AIHC), and the Canadian Interprofessional Health Collaborative (CIHC) have developed this e-book as a Discussion Paper to explore and discuss (from a global perspective) the impact and application of healthcare education and practice transformation on IPECP as we emerge from the COVID Pandemic with the goal to identify best practices to integrate and sustain IPECP. We call the interprofessional educators, practitioners, leaders, scholars, and policy makers to utilize ‘Forward Thinking and Adaptability’ and ‘Sustainability and Growth’ in their IPECP approaches and strategies, to achieve Quintuple Aim. As learned during the Pandemic, working together – across professions, institutions, nationally, and globally – is essential in emerging stronger and in transforming our healthcare education and practice.
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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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.027 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".