愤怒
潜在类模型
萧条(经济学)
临床心理学
心理学
睡眠(系统调用)
精神科
睡眠障碍
创伤应激
失眠症
数学
计算机科学
统计
操作系统
宏观经济学
经济
作者
Elizabeth L. Griffith,Ling Jin,Ateka A. Contractor,Danica C. Slavish,Anka A. Vujanovic
标识
DOI:10.1016/j.jpsychires.2022.06.052
摘要
Firefighters are at increased risk for posttraumatic stress disorder (PTSD) symptoms and sleep disturbances due to occupational trauma exposure as well as the nature of their job (e.g., shift work, workplace stress). PTSD symptoms co-occur with sleep disturbances, including poor sleep quality, short sleep duration, and low sleep efficiency. No published studies have examined subgroups of firefighters based on PTSD symptoms and sleep disturbances. Thus, we used latent profile analysis to identify the best-fitting class solution to categorize firefighters based on endorsed PTSD symptoms and sleep disturbances and examined relations between the optimal class solution and health covariates (i.e., anger reactions, depression symptoms, emotion regulation difficulties, number of traumatic event types). The sample included 815 trauma-exposed firefighters (Mage = 38.63; 93.20% male). Results indicated three latent subgroups: High PTSD-Sleep Disturbances, Moderate PTSD-Sleep Disturbances, and Low PTSD-Sleep Disturbances. Multinomial logistic regression indicated that endorsing greater anger reactions, depression symptoms, and emotion regulation difficulties increased the chances of being in the more severe classes. Endorsing greater number of traumatic event types increased the chances of being in the Moderate vs. Low PTSD-Sleep Disturbances Classes. Findings improve our understanding of subgroups of firefighters based on PTSD and sleep disturbances and underscore the importance of addressing depression symptoms, anger management, and emotion regulation skills for firefighters reporting more severe PTSD symptoms and sleep disturbances.
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