Supporting the support person: Oncologists' roles in reducing support people's uncertainty and facilitating psychological adjustment

Abstract

Objective: Support people of cancer patients are at significant risk for psychological distress. Additionally, cancer patients' well-being is reciprocally associated with support peoples' psychological well-being. Informed by Uncertainty in Illness Theory, this study tests whether support person psychological well-being is influenced by provider communication and uncertainty reduction.

Methods: We tested a multiple mediation model to investigate how empathic communication facilitates psychological adjustment in support people of cancer patients and how this process is mediated by support peoples' illness uncertainty and caregiver burden. Support people of cancer patients (N = 121; including spouses, adult children, etc.) completed an online questionnaire about their perceptions of oncologists' empathy, uncertainty about the cancer patients' illness, perceived caregiving burden, and their psychological adjustment to diagnoses.

Results: Path analysis revealed that (1) more perceived oncologist empathy was associated with less illness uncertainty, (2) more illness uncertainty was associated with worse psychological adjustment and more perceived caregiver burden, and (3) more burden was associated with worse adjustment (χ² (2) = 1.19, p = 0.55; RMSEA < 0.01; CFI = 1.00; SRMR = 0.02).

Conclusions: Given the reciprocal nature of well-being between cancer patients and their support people, it is critical to understand and bolster support people's psychological well-being. Results demonstrated how empathic provider communication can support psychological well-being for support people of cancer patients. Additionally, this study offers theoretical contributions to understandings of illness uncertainty in caregiver populations.

Keywords: cancer; caregiver; communication; psycho-oncology; psychological well-being; uncertainty.

FIGURE 1

(A) Hypothesized model and (B) alternative model.

FIGURE 2

Path analysis results for (A) the hypothesized model and (B) the alternative model. Parameter estimates are standardized. (A) Model fit indices χ²(2) = 1.19, p = 0.55; RMSEA < 0.01; CFI = 1.00; SRMR = 0.02. (B) Model fit indices χ²(1) = 1.06, p = 0.30; RMSEA = 0.02; CFI = 1.00; SRMR = 0.02. Model fit not significantly different across models (Δχ² = 0.13, Δdf = 1, p = 0.72). *p < 0.01.