Forward Thinking and Adaptability to Sustain and Advance IPECP in Healthcare Transformation Following the COVID-19 Pandemic
Bibliographic record
Abstract
The proliferation of the novel SARS-CoV-2 (COVID-19) virus across the globe in 2020 produced a shared trauma internationally of unprecedented devastation, disruption, and death. At the same time, the pandemic has been a transformation catalyst accelerating the implementation and adoption of long overdue changes in healthcare education and practice, including telehealth and virtual learning. The COVID-19 pandemic has placed healthcare at a crossroads, either viewing it as a temporary situation that requires short-term solutions, or as a major disruption that presents opportunities for innovation for sustainable development and transformation. As COVID-19 transitions from pandemic to endemic, we have a unique opportunity to leverage lessons learned that can foster healthcare transformation through innovation, forward thinking, and interprofessional education and collaborative practice (IPECP). With the changing landscape of higher education and healthcare, IPECP leaders need to reflect on and implement ‘Forward Thinking and Adaptability’ and ‘Sustainability and Growth’ in their IPECP approaches and strategies to achieve the Quintuple Aim. To capitalize on this opportunity and based on a recent publication by InterprofessionalResearch Global, this paper explores and debates (from a global perspective) the impact and application of healthcare education and practice transformation on IPECP with the goal to identify best practices in integrating and sustaining IPECP and building a resilient workforce.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.094 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".