Transdisciplinary Scholarship Connector: $8,491
Principal Investigator: Dr Gregory Tweedie
Co-investigator: Dr Meagan LaRiviere
Co-investigator: Crystal Lawrence
Project description: Canada endures a chronic shortage of nurses – a reality especially evident during the COVID-19 pandemic (Lopez et al, 2022) – and the recruitment and deployment of internationally educated nurses (IENs) as one means of filling this gap is a feature of health policy across provinces, including Alberta (Nordstrom et al. 2018). However, an inconsistent, unclear, haphazard and bureaucratic process of credentialing, particularly the procedure for determining equivalency of nursing competencies and qualifications, often results in long periods of workforce deployment: one study showed an average length of 656 days for the registration process (Giblin et al, 2016). Even after a substantial study and policy interventions, the total length of time did not substantially change except for those applicants who opted to bypass the assessment process and move right into a nurse education bridging program (Giblin, et al. 2016).
The process of determining IENs’ eligibility for nursing practice in Canada involves substantial equivalent competency assessment (SEC; see Kwan et al, 2017) that compares prior education and experience in the IEN’s home country to entry-level Canadian nursing competencies.
Artificial intelligence / machine learning technology holds promise to significantly reduce delays in the SEC process. A machine learning model trained to map prior qualifications to Alberta nursing competencies, presented in an accessible user interface which IENs can access in their home country at a fraction of the cost, will significantly reduce time to workforce deployment of qualified IENs, reducing the strain on our healthcare system, and on IENs themselves, who as newcomers to Canada are vulnerable to under-/unemployment and the accompanying challenges, while awaiting the SEC process.