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Using Narratives to Correct Forecasting Errors in Pediatric Tracheostomy Decision Making
Haoyang Yan1, Patricia J Deldin1,2, Stephanie K Kukora3
1Department of Psychology, University of Michigan, Ann Arbor, MI, USA.
Purpose:
Parents who face goals-of-care tracheostomy decisions may lack an understanding of challenges affecting their child's and family's long-term quality of life (QOL) to accurately forecast possible outcomes for decision making. We sought to examine whether and how parents' narratives of the child's and family's long-term QOL influence parental tracheostomy decisions and forecasting.
Method:
We recruited US adult Amazon Mechanical Turk participants (N = 1966) who self-reported having a child (<6 y old) or planning a pregnancy within 5 y. Participants read a vignette about making a tracheostomy decision for their hypothetical neurologically impaired baby. They were randomized to 1 of the following 4 conditions: 1) Baby QOL narratives, 2) Family QOL narratives, 3) Baby QOL + Family QOL narratives, and 4) control: no narratives. They then made a decision about whether or not to pursue tracheostomy, forecasted their concerns about the baby's and family's QOL, reported their values and social norm beliefs about tracheostomy, comfort care, and parental medical decision making, and completed individual differences scales and demographics.
Results:
Controlling for individual characteristics, participants in the Baby QOL and Baby QOL + Family QOL conditions were less likely to choose tracheostomy as compared with the control (odds ratio [OR] = 0.38 and 0.25, respectively, P < 0.001). Fewer participants in the Family QOL condition chose tracheostomy compared with the control, but this difference was not statistically significant (OR = 0.70, P = 0.11). Moreover, narratives increased pessimistic forecasting, which was associated with less interest in tracheostomy.
Conclusion:
Narratives clarifying long-term implications of pursuing tracheostomy have the potential to influence forecasting and decisions. Narrative-based interventions may be valuable in other situations in which forecasting errors are common.
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