PDF(Pubmed)
Abstract：
Sensory processing sensitivity (SPS) is a personality trait that makes certain individuals excessively sensitive to stimuli. People carrying this trait are defined as Highly Sensitive People (HSP). The SPS trait is notably prevalent among nursing students and nurse staff. Although there are HSP diagnostic tools, there is little information about early detection. Therefore, the aim of this work was to develop a prediction model to identify HSP and provide an individualized nursing assessment. A total of 672 nursing students completed all the evaluations. In addition to the HSP diagnosis, emotional intelligence, communication skills, and conflict styles were evaluated. An interpretable machine learning model was trained to predict the SPS trait. We observed a 33% prevalence of HSP, which was higher in women and people with previous health training. HSP were characterized by greater emotional repair (p = 0.033), empathy (p = 0.030), respect (p = 0.038), and global communication skills (p = 0.036). Overall, sex and emotional intelligence dimensions are important to detect this trait, although personal characteristics should be considered. The present individualized prediction model could help to predict the presence of the SPS trait in nursing students, which may be useful in conducting intervention strategies to avoid the negative consequences and reinforce the positive ones of this trait.
摘要：
感觉处理敏感性（SPS）是一种人格特质，使某些人对刺激过于敏感。具有这种特征的人被定义为高度敏感的人(HSP)。SPS特征在护生和护理人员中尤为普遍。虽然有HSP诊断工具，关于早期检测的信息很少。因此,这项工作的目的是开发一种预测模型来识别HSP并提供个性化的护理评估.共有672名护生完成了所有评估。除了HSP诊断，情商,沟通技巧,并对冲突风格进行了评估。训练可解释的机器学习模型来预测SPS特征。我们观察到33%的HSP患病率，在女性和以前接受过健康培训的人中，这一比例更高。HSP的特点是更大的情绪修复（p=0.033），移情(p=0.030)，尊重（p=0.038），和全球沟通技巧(p=0.036)。总的来说,性和情绪智力维度对检测这种特征很重要，虽然个人特征应该被考虑。目前的个性化预测模型可以帮助预测护理专业学生SPS特征的存在。这可能有助于实施干预策略，以避免负面后果并加强这种特征的积极后果。
