• Andrew L Walker, Emily Marchi, Ana Puig, William Slayton, Virginia Dodd, Megan A McVay, and Melvin D Livingston.
• Department of Behavioral, Social, and Health Education Sciences, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA.
• J Palliat Med. 2022 Aug 1; 25 (8): 118611961186-1196.

AbstractObjective: To conduct a social network analysis (SNA) of patient-volunteer networks and assess the impact of patient characteristics on network measures. Background: Volunteers play a critical role in providing peer support to adolescent and young adult (AYA) palliative care patients. Streetlight at UF Health is a peer support palliative care program for hospitalized AYAs that aims at forming positive peer relationships through volunteer visits, events, and a virtual online health community. Methods: Data were collected on patient characteristics, hospitalizations, average length of stays (LOS), and volunteer visitation records. Egocentric SNAs were conducted on each patient to calculate network outcomes. Study participants were AYA patients (N = 69), enrolled in the US-based Streetlight program at UF Health Shands Hospital. Results: The LOS was significantly associated with network size (B = 0.583; 95% confidence interval; CI [0.463 to 0.702]). Autoimmune patients had smaller network sizes when controlling for LOS. Total hospital admissions predicted - 0.172 ([- 0.263 to - 0.080]) lower average repeat visits. Higher average repeat visits were predicted for patients who had cancer (B = 0.246 [0.046 to 0.447]) and awaiting organ transplantation (B = 0.370 [0.082 to 0.658]). Although cystic fibrosis patients received more visits (B = 0.364 [0.003 to 0.724]) compared with other illness populations, the network density was lower (B = - 0.580 [1.01 to - 0.155]). Cancer patients had networks with a higher diversity in volunteer repeat visits (B = 0.714 [0.312 to 0.920]). Conclusions: Significant relationships between patient characteristics and network outcomes highlight the differences in social support service delivery among diverse populations. These analyses can be utilized in practice to guide program delivery for high-need patients.

15 keywords...

hide…