No abstract

This qualitative study begins to explore cancer survivors’ evolving perceptions of “Focus on the Future,” a 6-week supportive virtual program led by trained volunteers and health care professionals. Through purposive sampling, participants (n = 10) enrolled in the program were individually interviewed shortly before attending, mid-way through, and at program completion. Interviews were digitally recorded and transcribed verbatim. Thematic analysis was used to develop key elements of program expectations and users’ perceptions over time. Three themes transpired from the data: (1) Trustworthiness and timeliness of survivorship information and expert guidance, (2) Normalization of survivors’ experiences, and (3) Virtual program delivery issues. Some participants’ perceptions remained unchanged from pre-program expectations to post-program completion such as appreciating the efficiency of virtual delivery and “health safe” exchanges given the COVID-19 pandemic. In contrast, other perceptions became more polarized including drawbacks related to “more superficial” virtual connections and uneven topic relevance as the program evolved. Program participants appreciated timely information and support from volunteers and experts through virtual means and consecutive weekly sessions. Gauging participants’ perceptions across time also offer opportunities to adjust program content and delivery features. Future research should explore key program development strategies to ensure that cancer supportive programs are optimally person-centered, co-designed, and situation-responsive.

Enforceable Community Rights and National Remedies

For mobile robots, such as Autonomous Surface Vessels (ASVs), limiting error from a target trajectory is necessary for effective and safe operation. This can be difficult when subjected to environmental disturbances like wind, waves, and currents. This work compares the tracking performance of an ASV using a Model Predictive Controller that includes a model of these disturbances. Two disturbance models are compared. One prediction model assumes the current disturbance measurements are constant over the entire prediction horizon. The other uses a statistical model of the disturbances over the prediction horizon. The Model Predictive Controller performance is also compared to a PI-controlled system under the same disturbance conditions. Including a disturbance model in the prediction of the dynamics decreases the trajectory tracking error over the entire disturbance spectrum, especially for longer horizon lengths.