BACKGROUND: Previous research indicates that children can display different attention allocation patterns in response to threat. However, data are lacking on the possible existence of an attentional bias in response to academic stressors, and whether variables related to school well-being (SWB) and students' individual characteristics may influence such attentional patterns. AIM: We aimed to investigate whether students show an attentional bias for school-related stressors. We sought to identify groups of students who differ in their perceived SWB, and to test whether they display different attention allocation patterns. We also examined whether negative emotionality moderates the expected association between students' SWB and attentional bias. METHODS: Eighth-grade students completed a dot-probe detection task to register attentional patterns towards or away from an academic threatening word. SWB in terms of school anxiety, school-based stress, and perceived class climate was also assessed via self-reports. In addition, participants reported on their negative emotionality. RESULTS: The results indicated that students showed an attentional bias towards words describing academic stressors. Cluster analyses allowed the identification of two groups of students with high versus low SWB. Regression analysis indicated that those with low SWB were more likely to show a greater bias and that negative emotionality moderated this relationship. Specifically, within the context of low SWB, students with high negative emotionality were more prone to biased attention towards school-related stressors compared with students with low negative emotionality. CONCLUSIONS: The present data indicate a perceptual bias for the detection of academic threats. Within the school practice, teachers should promote SWB and devote specific attention to students with high negative emotionality to reduce a biased allocation of attention in response to school-related stressors.
BACKGROUND: Previous research indicates that children can display different attention allocation patterns in response to threat. However, data are lacking on the possible existence of an attentional bias in response to academic stressors, and whether variables related to school well-being (SWB) and students' individual characteristics may influence such attentional patterns. AIM: We aimed to investigate whether students show an attentional bias for school-related stressors. We sought to identify groups of students who differ in their perceived SWB, and to test whether they display different attention allocation patterns. We also examined whether negative emotionality moderates the expected association between students' SWB and attentional bias. METHODS: Eighth-grade students completed a dot-probe detection task to register attentional patterns towards or away from an academic threatening word. SWB in terms of school anxiety, school-based stress, and perceived class climate was also assessed via self-reports. In addition, participants reported on their negative emotionality. RESULTS: The results indicated that students showed an attentional bias towards words describing academic stressors. Cluster analyses allowed the identification of two groups of students with high versus low SWB. Regression analysis indicated that those with low SWB were more likely to show a greater bias and that negative emotionality moderated this relationship. Specifically, within the context of low SWB, students with high negative emotionality were more prone to biased attention towards school-related stressors compared with students with low negative emotionality. CONCLUSIONS: The present data indicate a perceptual bias for the detection of academic threats. Within the school practice, teachers should promote SWB and devote specific attention to students with high negative emotionality to reduce a biased allocation of attention in response to school-related stressors.
Authors: Aitana Fernández-Sogorb; Ricardo Sanmartín; María Vicent; Carolina Gonzálvez Journal: Int J Environ Res Public Health Date: 2021-01-22 Impact factor: 3.390