A Systematic Review of the Outcome Measures Used to Evaluate Interprofessional Learning by Health Care Professional Students During Clinical Experiences.
Nicole Ann GuitarDenise M ConnellyPublished in: Evaluation & the health professions (2020)
Interprofessional education (IPE) occurs when members of more than one health or social care profession learn interactively together to improve interprofessional collaboration and health care delivery. Interprofessional experiences provide students with the skills and knowledge needed to work in a collaborative manner; however, there is no review on the outcome measures used to assess the effectiveness of IPE learning. The current systematic review examined the outcome measures used to assess interprofessional learning during student clinical experiences. An electronic search of databases retrieved trials of health professional students who completed an IPE intervention during a student clinical experience. Methodological quality of twenty-five studies meeting the inclusion criteria published between 1997 and 2018 was scored independently by two raters using the Physiotherapy Evidence Database and the Confidence in the Evidence from Reviews of Qualitative Research tool. The Interdisciplinary Education Perception Scale was used most frequently to assess interprofessional learning during a student clinical experience. This review provides a summary of outcome measures for educators to consider for evaluation of interprofessional activities during student clinical placements and serves to inform future conversations regarding the use and development of outcome measures to provide evidence for student achievement of IPE objectives and competencies.
Keyphrases
- healthcare
- high school
- patient safety
- systematic review
- quality improvement
- nursing students
- mental health
- medical students
- randomized controlled trial
- public health
- medical education
- meta analyses
- health information
- palliative care
- risk assessment
- emergency department
- chronic pain
- big data
- deep learning
- affordable care act
- electronic health record
- adverse drug