School non-attendance and learned helplessness: latent profiles and ROC curves

Due to the complex school reality, Learned Helplessness (LH) is a student's response characterized by lack of confident, interpretative bias and negative outlook of success in face of school challenges. These helpless students develop a negative attitude toward school, leading to a withdrawal of school engagement and anxious disorders, reporting links with emotionally based school non-attendance. Taking into account the heterogeneous causes of these problems, in recent years new instruments have been emerged, like <i>Assessing Reasons for School Non-Attendance</i> (ARSNA; Havik et al., 2015), of which there is a clear lack of research. The study aims to: (1) identify latent profiles of school absenteeism based on <i>Assessing Students' Reported Reasons for School Non-attendance</i> (ARSNA; Havik et al., 2015); (2) analyze differences between school non-attendance profiles and Learned Helplessness (LH); and (3) establish the predictive and discriminative capacity of LH to identify students of the high school non-attendance profile. Consequently, 759 adolescents (<i>M</i> = 14.95, <i>SD</i> = 1.82) fulfilled ARSNA (Havik et al., 2015) and <i>Learned Helplessness Questionnaire</i> (LHQ; Sorrenti et al., 2015). Pearson's correlation coefficients reported positive and statistically significant correlations between ARSNA dimensions and LH. Latent Profile Analyses revealed 3 school absenteeism profiles. ANOVA indicated statistically significant differences between these profiles and LH. Finally, Logistic Regression and ROC Curves found the predictive and discriminative ability of LH to identify individuals of the high school non-attendance profile. Results contribute to the literature on ARSNA dimensions and LH, highlighting the potential implications for schools and for the intervention against emotionally based school non-attendance.

Authors

Institutions

Publication Details

Journal
Frontiers in Psychology
Published
2025-06-18
DOI
https://doi.org/10.3389/fpsyg.2025.1557915
Primary Topic
Youth Substance Use and School Attendance
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

School non-attendance and learned helplessness: latent profiles and ROC curves

María Pérez‐Marco, Andrea Fuster‐Rico, Carolina Gonzálvez
Frontiers in Psychology
Youth Substance Use and School Attendance
article

School non-attendance and learned helplessness: latent profiles and ROC curves

María Pérez‐Marco, Andrea Fuster‐Rico, Carolina Gonzálvez
article en

Abstract

Due to the complex school reality, Learned Helplessness (LH) is a student's response characterized by lack of confident, interpretative bias and negative outlook of success in face of school challenges. These helpless students develop a negative attitude toward school, leading to a withdrawal of school engagement and anxious disorders, reporting links with emotionally based school non-attendance. Taking into account the heterogeneous causes of these problems, in recent years new instruments have been emerged, like <i>Assessing Reasons for School Non-Attendance</i> (ARSNA; Havik et al., 2015), of which there is a clear lack of research. The study aims to: (1) identify latent profiles of school absenteeism based on <i>Assessing Students' Reported Reasons for School Non-attendance</i> (ARSNA; Havik et al., 2015); (2) analyze differences between school non-attendance profiles and Learned Helplessness (LH); and (3) establish the predictive and discriminative capacity of LH to identify students of the high school non-attendance profile. Consequently, 759 adolescents (<i>M</i> = 14.95, <i>SD</i> = 1.82) fulfilled ARSNA (Havik et al., 2015) and <i>Learned Helplessness Questionnaire</i> (LHQ; Sorrenti et al., 2015). Pearson's correlation coefficients reported positive and statistically significant correlations between ARSNA dimensions and LH. Latent Profile Analyses revealed 3 school absenteeism profiles. ANOVA indicated statistically significant differences between these profiles and LH. Finally, Logistic Regression and ROC Curves found the predictive and discriminative ability of LH to identify individuals of the high school non-attendance profile. Results contribute to the literature on ARSNA dimensions and LH, highlighting the potential implications for schools and for the intervention against emotionally based school non-attendance.

Frontiers in PsychologyVol. 16
University of Alicante (ES)
Ministerio de Ciencia, Innovación y Universidades
Reduced inequalities
Openalex Percentile: Top 85%
Youth Substance Use and School Attendance
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.