An Interpretable Machine Learning Approach to Predict Sensory Processing Sensitivity Trait in Nursing Students.
Eur J Investig Health Psychol Educ
; 14(4): 913-928, 2024 Apr 02.
Article
em En
| MEDLINE
| ID: mdl-38667814
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
Eur J Investig Health Psychol Educ
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Espanha