Belisle Jordan, Paliliunas Dana, Sickman Elana, Janota Taylor, Lauer Taylor
Psychology Department, Missouri State University, 01 South National Avenue, Springfield, MO 65897 USA.
Psychol Rec. 2022;72(4):713-725. doi: 10.1007/s40732-022-00527-9. Epub 2022 Sep 5.
The present study was a preliminary analysis of college students' willingness to self-isolate and socially isolate during the COVID-19 pandemic analyzed through a probability discounting framework. Researchers developed a pandemic likelihood discounting task where willingness to isolate from others was measured in days as a function of the perceived probability of the escalation of a virus to pandemic levels. Experiment 1 was conducted immediately prior to the World Health Organization (WHO) declaring COVID-19 a pandemic and results showed that participants were more willing to self-isolate when the perceived probability of reaching pandemic levels was high and when there was a guarantee that others in the community would do the same. Experiment 2 was conducted with a subset of participants from Experiment 1 with the same discounting task, and results showed that participants were more willing to self-isolate 2 months following the onset of the pandemic, supporting the view that willingness to isolate from others is a dynamic process. Finally, Experiment 3 evaluated willingness to socially distance and introduced a hypothetical timescale to evaluate common trends with the real-world temporal dynamics observed in Experiments 1 and 2. Results showed similar trends in the data, supporting the use of hypothetical scenarios within probability discounting tasks in future behavior analytic research related to public health.
本研究是通过概率折扣框架对大学生在新冠疫情期间自我隔离和社交隔离意愿的初步分析。研究人员开发了一项疫情可能性折扣任务,其中与他人隔离的意愿以天数衡量,作为病毒升级到疫情水平的感知概率的函数。实验1在世界卫生组织(WHO)宣布新冠疫情大流行之前立即进行,结果表明,当达到疫情水平的感知概率较高且社区中的其他人也会这样做得到保证时,参与者更愿意自我隔离。实验2对实验1的一部分参与者进行了相同的折扣任务,结果表明,在疫情爆发2个月后,参与者更愿意自我隔离,这支持了与他人隔离的意愿是一个动态过程的观点。最后,实验3评估了社交距离的意愿,并引入了一个假设的时间尺度来评估与实验1和2中观察到的现实世界时间动态的共同趋势。结果显示数据中有类似趋势,支持在未来与公共卫生相关的行为分析研究中,在概率折扣任务中使用假设情景。