Cognitive predictors of grief trajectories in the first months of loss: A latent growth mixture model.
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ABSTRACT: OBJECTIVE:The identification of modifiable cognitive antecedents of trajectories of grief is of clinical and theoretical interest. METHOD:The study gathered 3-wave data on 275 bereaved adults in the first 12-18 months postloss (T1 = 0-6 months, T2 = 6-12 months, T3 = 12-18 months). Participants completed measures of grief severity, cognitive factors (loss-related memory characteristics, negative appraisals, unhelpful coping strategies, and grief resilience), as well as measures of interpersonal individual differences (attachment and dependency). Latent growth mixture modeling was used to identify classes of grief trajectories. Predictors of class membership were identified using multinomial logistic regression and multigroup structural equation modeling. RESULTS:Four latent classes were identified: 3 high grief classes (Stable, Low Adaptation, and High Adaptation) and a low grief class (Low Grief). When considered separately, variance in all four cognitive factors predicted membership of the high grief classes. When considered together, membership of the high grief classes was predicted by higher mean scores on memory characteristics. More negative appraisals predicted low or no adaptation from high grief severity. Losing a child also predicted membership to the stable class. Fast adaptation of high grief was predicted by a pattern of high memory characteristics but low engagement with unhelpful coping strategies. CONCLUSIONS:The findings have implications for clinical practice and point to early cognitive predictors of adaptation patterns in grief. Findings are consistent with cognitive models highlighting the importance of characteristics of memory, negative appraisals, and unhelpful coping strategies in the adaptation to highly negative life events. (PsycINFO Database Record (c) 2020 APA, all rights reserved).
SUBMITTER: Smith KV
PROVIDER: S-EPMC6939605 | biostudies-literature | 2020 Feb
REPOSITORIES: biostudies-literature
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