Future-Proof Healthcare Professionals: Innovative Approaches from Canada, The Netherlands, and The United States of America
Bibliographic record
Abstract
Innovation skills are part of 21st century skills, a broad skillset that supports healthcare professionals to better sustain, or future-proof, a career in the modern workplace. Educational programs and services in healthcare need to prepare students to innovate in order to address complex needs of aging and changing demographics in global populations. Early career healthcare professionals will benefit from skills and adaptability to tackle challenges and innovate their practices. Engagement in innovation that involves technology cultivates such new skill sets, fosters leadership, and positions these healthcare professionals as critical players in shaping the future of their professions. In this article, we describe examples of integrating 21st century skills into three categories: curriculum and instruction, professional development, and learning environments, based on a partnership framework for 21st century learning. Our shared examples offer an international perspective on the topic of innovation, describing efforts within occupational therapy, nursing, and social work programs in Canada, The Netherlands, and the USA. In the area of curriculum and instruction, we include initiatives to build innovation skills both within a single course and integrated opportunities throughout the curricula. From the professional development perspective, new professional roles and programs have emerged with a focus on technology. Finally, educational support systems have fostered innovative learning environments for interdisciplinary education with a focus on collaboration, innovation, and creativity. In all examples, interprofessional collaboration between education and practice was a leading strategy to prepare healthcare professionals across the globe with necessary 21st century leadership skills for innovative practice.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".