Previous studies have demonstrated an association between low personal protective equipment (PPE) availability and high stress and anxiety among frontline healthcare workers during the COVID-19 pandemic. It is unclear how other factors, such as infection prevention and control (IPC) training and IPC policy support, correlate with workers’ distress. The current study explores these relationships. We conducted a secondary analysis of a public survey dataset from Statistics Canada. Acute care workers’ survey responses (n = 7379) were analyzed using structural equation modeling to examine relationships between features of the IPC work environment and acute care workers’ ratings of their stress and mental health. We found that PPE availability (β = −0.16), workplace supports (i.e., training, IPC policy compliance, and enforcement) (β = −0.16), and support for staying home when sick (β = −0.19) were all negatively correlated with distress. Together, these features explained 18.4% of the overall variability in workers’ distress. Among surveyed acute care workers, PPE availability was related to their distress; however, having workplace support and an emphasis on staying home when sick was also relevant. Overall, the results highlight that, in addition to PPE availability, workplace supports and emphasis on staying home are important. IPC professionals and healthcare leaders should consider these multiple features as they support acute care workers during future infectious disease outbreaks.

Over the last decade, Canadian students have exhibited insubstantial improvements in mathematical scores compared to other countries as indicated by large-scale educational assessments such as the Programme for International Student Assessment (PISA) and the Trends in International Mathematics and Science Study (TIMSS). In relation to students’ mathematical performance, math anxiety - the feeling of fear or nervousness when performing math-related tasks - was found as an associated factor. However, no previous study has explored math performance and math anxiety, specifically among Albertan students. We present a work-in-progress that identifies significant predictors of math performance and math anxiety among Canadian and Albertan students, using the PISA 2018 and TIMSS 2019 datasets. This study has three phases: first, a list of predictors will be selected from the data set based on existing theories regarding students’ math performance and math anxiety. The initial list of predictors will be presented to domain experts (i.e., math teachers) for refinement based on their practical experience. A predictive model for math performance and math anxiety will be developed with Educational Data Mining techniques in the second phase. Results from the model will be presented to the domain experts for their inputs as the qualitative component, and variable importance metrics of the model will be consulted for the quantitative component. Findings from both components will be integrated consulted with the domain experts to derive actionable recommendations that would inform various stakeholders (e.g., educators, school districts, and Alberta Education) of ways to improve math performance in Alberta students.