Objective: Patients undergoing cancer treatment during the COVID-19 pandemic have experienced stress and uncertainty with respect to disruptions in cancer care and COVID-19 related risks. We examined whether clinicians' responsiveness to patients' uncertainty and difficult emotions were associated with better health and well-being.
Methods: Patients were recruited from cancer support communities and a market research firm. Respondents assessed clinicians communication that addressed uncertainty and difficult emotions. Health status measures included mental and physical health, coping during the pandemic, and psychological distress.
Results: 317 respondents participated in the study. Patients' perceptions of their clinicians responsiveness to patient uncertainty and negative emotions were associated with better mental health, physical health, coping, and less psychological distress (all p-values <0.001). Respondents with greater self-efficacy and social support also reported better health.
Conclusion: Even when controlling for patients' personal and health-related characteristics, clinicians' communication addressing patients' uncertainty and difficult emotions predicted better health, better coping, and less psychological distress. Access to social support and self-efficacy also were associated with better health status.
Practice Implications: Clinicians' communication focused on helping with uncertainty and difficult emotions is important to cancer patients, especially during the pandemic. Clinicians should also direct patients to resources for social support and patient empowerment.
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http://dx.doi.org/10.1016/j.pec.2022.03.029 | DOI Listing |
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Centre for Neuroscience Studies, Queen's University, Kingston, ON, Canada.
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December 2024
Henan University of Engineering, Zhengzhou, 451191, China.
Social media generates vast amounts of spatio-temporal sequential data. However, current methods often ignore the complex spatio-temporal correlations within these data. This oversight makes it difficult to fully capture the dynamic features of the data.
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