Professional inclusivity: Creating faculty development learning environments to facilitate learning across the health professions
Polansky, M. N.; de Nooijer, J.; Fabry, G.
Show abstract
BackgroundLearning environments involving multiple health professions may provide opportunities for interprofessional learning (IPL). However, not all multi-professional settings provide the necessary conditions to support IPL. Using the concept of professional inclusivity, the purpose of this study was to explore conditions that support professionally inclusive learning environments in faculty development, using the example of Master in Health Professions Education (MHPE) programs. MethodsSemi-structured interviews with 14 students and faculty from four MHPE programs were conducted. Member checking of key themes was performed to enhance validity. ResultsOrganizing principles for supporting professionally inclusive learning environments were identified: (1) being intentional, (2) leveling the playing field, and (3) focusing on commonalities. Intentionality refers to the essential role of faculty in considering how each aspect of the program can impact inclusivity. Leveling the playing field refers to establishing a culture where students (and often faculty) are seen as equal regardless of profession. Commonalities relates to the common background of students, in both clinical and teaching experiences, and their shared educational needs. ConclusionsOrganizing principles for fostering professional inclusivity in faculty development learning environments were identified. Specific recommendations for the application within MHPE programs are provided. Professional inclusivity may be of significant valuable as a conceptual framework for further research regarding IPL.
Matching journals
The top 1 journal accounts for 50% of the predicted probability mass.
Similar papers in this journal
- Effect of introducing interprofessional education concepts on students of various healthcare disciplines in the United Arab Emirates 97%
- Improving capacity for advanced training in obstetric surgery: Evaluation of a blended learning approach 95%
- Perceptions, attitudes, and challenges regarding continuing professional development among Ethiopian Medical Laboratory professionals: A mixed-method study 94%
Similar papers in this journal
- Introducing the 4Ps Model of Transitioning to Distance Learning: a convergent mixed methods study conducted during the COVID-19 pandemic 97%
- The experiences, challenges, and enablers for promoting interprofessional education among medical students: A Scoping Review 96%
- A national professional development program fills mentoring gaps for postdoctoral researchers 96%
Similar papers in this journal
- Emotional Distress, Stress, Anxiety and the Impact of the COVID-19 Pandemic on Early Career Women in Healthcare Sciences Research 94%
- An e-Leadership Training Academy for Practicing Clinicians in Primary Care and Public Health Settings 93%
- A Retrospective Case Study of Successful Translational Research: Cardiovascular Disease Risk Assessment, Experiences in Community Engagement 92%
Similar papers in this journal
- Conceptualizing centers of excellence: A global evidence 95%
- How can rural community-engaged health services planning affect sustainable health care system changes? - A process description and qualitative analysis of data from the Rural Coordination Centre of British Columbia’s Rural Site Visits Project 94%
- The preparedness and response to COVID-19 in a quaternary Intensive Care Unit in Australia: perspectives and insights from frontline critical care clinicians 93%
Similar papers in this journal
- COVID-19 critical care simulations: An international cross-sectional survey 94%
- The Influence of Public Health Faculty on College and University Plans during the COVID-19 Pandemic 92%
- Cross-sector Decision Landscape in Response to COVID-19: A Qualitative Analysis of North Carolina Decision-Makers 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.