Assessing the influence of the modifiable areal unit problem on Bayesian disease mapping in Queensland, Australia.

Background: Spatial data are often aggregated by area to protect the confidentiality of individuals and aid the calculation of pertinent risks and rates. However, the analysis of spatially aggregated data is susceptible to the modifiable areal unit problem (MAUP), which arises when inference varies with boundary or aggregation changes. While the impact of the MAUP has been examined previously, typically these studies have focused on well-populated areas.

Bayesian Spatial Relative Survival Model to Estimate the Loss in Life Expectancy and Crude Probability of Death for Cancer Patients.

To date, there have not been any population-based cancer studies quantifying geographical patterns of the loss in life expectancy (LLE) and crude probability of death due to cancer ( ). These absolute measures of survival are complementary to the more typically used relative measures of excess mortality and relative survival, and, together, they provide a fuller understanding of geographical disparities in survival outcomes for cancer patients. We propose using a spatially flexible parametric relative survival model in the Bayesian framework, which allows for the inclusion of spatial effects in hazard-level model components.

'Changing People's Hearts': The Lived Expertise Perspective of Communicating With Nursing Students at a Mental Health Clinical Placement.

Escalating rates of mental illness emphasise the necessity for sufficient and appropriate mental health services. However, stigma and discrimination remain and can be seen through the multifaceted ways nurses communicate. Clinical placements, where nursing students engage directly with individuals experiencing mental illness, are vital for addressing these challenges by fostering empathy and reducing stigma.

"Coming Home Is the Hardest Part": An Interpretative Phenomenological Analysis of Sense Making in Military Postdeployment Reintegration.

The purpose of the present interpretative phenomenological analysis (IPA) study was to understand how military service members and veterans (MSMVs) make sense of their reintegration experiences following deployment. IPA provides the ability to gain a deeper understanding of a shared experience, or phenomenon, such as reintegration following deployment. Data collection involved semi-structured interviews via Zoom.

Cancer-related health behaviors during the COVID 19 pandemic in geographically diverse samples across the US.

Background: The COVID-19 pandemic involved business closures (e.g., gyms), social distancing policies, and prolonged stressful situations that may have impacted engagement in health behaviors.