Title: Linguistic Context Sensitivity as a Predictor of Prolonged Grief Symptoms
Description: Following the loss of a loved one, grief is a near-universal experience. While most grieving individuals are able to cope effectively and return to baseline functioning over time, some develop persistent complex bereavement disorder (PCBD). This dissertation aims to elucidate the ways in which cognitive and emotional processing go awry in the context of PCBD. More specifically, it examines the relationship between the types of language that bereaved individuals use and their trajectories of adjustment in the first year following a loss. In particular, this dissertation examines context sensitivity in word use. Context sensitivity describes the degree to which an individual is attuned to the particular demands of a given task or situation. In the present study, linguistic context-sensitivity was measured by analyzing the use of words pertaining to certain categories within specific contexts (e.g., participants were asked to discuss a recent positive event, and the use of positive emotion words within their responses was analyzed).

Results indicated that, among those individuals who display high levels of grief immediately following the loss of their spouse, the use of context-sensitive language predicted a favorable course of adjustment in the first year of bereavement with low levels of grief at one- year post-loss. Conversely, the use of context insensitive language predicted high levels of grief at one-year post-loss. These findings indicate that context sensitivity may instrumental in the development of PCBD and, as such, may help predict an individual’s course of adjustment immediately following a significant loss. A better understanding of these early signs of PCBD may greatly assist in the timely detection of the disorder so that intervention may be most effective. Furthermore, this field of inquiry also has the potential to deeply inform treatment modalities designed to help individuals cope in the wake of bereavement.