Grant: Project Grant

The COVID-19 pandemic has made clear the dire need to address underlying long-term care (LTC) workforce and workplace issues that affect resident quality of care and quality of life. These issues include insufficient funding, under-staffing, poor working conditions, complacency towards overburdened and undervalued staff, and lack of regulatory enforcement. These issues also contribute to poor job satisfaction among workers. In recent years, there have been policy changes to improve workforce and workplace conditions. But, some of these 'fixes' were later shown to be ineffective or even made the problem worse. These 'fixes that fail' are not an uncommon occurrence in complex systems like healthcare. The interconnectedness of factors, the nonlinearity of relationships (can't draw a straight line between two variables), the feedback processes created in response to policy changes, and delayed reactions all make for a complex system that common quantitative tools used in policy design and evaluation, such as spreadsheet models and regression analysis, have difficulty handling. The proposed research uses novel engineering approaches, including group model building, to create a dynamic model of LTC job satisfaction. Our proposed model relies on quantitative and qualitative data available only from LTC homes. We also rely on the expertise of personal support workers to help specify causal relationships between variables. We will test how well the model replicates historical data from a LTC home. We will then use the model to test 'what if' policy scenarios. Policy makers can graphically see how outcomes, like job satisfaction, might respond over time to hypothetical policies. They can also trace the causal pathway between policy and outcome to better understand which variables have most impact. While real world interventions are needed, the proposed research can be a first step to designing and improving the LTC system.